Methods and systems for detection in an industrial internet of things data collection environment with adjustment of detection parameters for continuous vibration data

ABSTRACT

Methods and systems for a monitoring system for data collection in an industrial environment including a data storage structured to store detection parameters for a plurality of input channels; a data collector communicatively coupled to the plurality of input channels, wherein the data collector collects data from the plurality of input channels based on the detection parameters; a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each of the plurality of detection values corresponding to at least one of the plurality of input channels; and a data analysis circuit structured to analyze the collected data from the plurality of input channels, wherein one of the plurality of input channels is connected to a vibration sensor providing continuous vibration data.

CROSS-REFERENCE TO RELAYED APPLICATIONS

The present application claims the benefit of, and is a continuation of,U.S. Non-Provisional patent application Ser. No. 15/973,406, filed May7, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIALINTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGE DATA SETS(STRF-0001-U22).

U.S. Ser. No. 15/973,406 (STRF-0001-U22) is a bypasscontinuation-in-part of International Application Number PCT/US17/31721,filed May 9, 2017, entitled METHODS AND SYSTEM FOR THE INDUSTRIALINTERNET OF THINGS, published on Nov. 16, 2017, as WO 2017/196821(STRF-0001-WO), which claims priority to: U.S. Provisional PatentApplication Ser. No. 62/333,589, filed May 9, 2016, entitled STRONGFORCE INDUSTRIAL IOT MATRIX (STRF-0001-P01); U.S. Provisional PatentApplication Ser. No. 62/350,672, filed Jun. 15, 2016, entitled STRATEGYFOR HIGH SAMPLING RATE DIGITAL RECORDING OF MEASUREMENT WAVEFORM DATA ASPART OF AN AUTOMATED SEQUENTIAL LIST THAT STREAMS LONG-DURATION ANDGAP-FREE WAVEFORM DATA TO STORAGE FOR MORE FLEXIBLE POST-PROCESSING(STRF-0001-P02); U.S. Provisional Patent Application Ser. No.62/412,843, filed Oct. 26, 2016, entitled METHODS AND SYSTEMS FOR THEINDUSTRIAL INTERNET OF THINGS (STRF-0001-P03); and U.S. ProvisionalPatent Application Ser. No. 62/427,141, filed Nov. 28, 2016, entitledMETHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS(STRF-0001-P04).

U.S. Ser. No. 15/973,406 (STRF-0001-U22) also claims priority to: U.S.Provisional Patent Application Ser. No. 62/540,557, filed Aug. 2, 2017,entitled SMART HEATING SYSTEMS IN AN INDUSTRIAL INTERNET OF THINGS(STRF-0001-P05); U.S. Provisional Patent Application Ser. No.62/562,487, filed Sep. 24, 2017, entitled METHODS AND SYSTEMS FOR THEINDUSTRIAL INTERNET OF THINGS (STRF-0001-P06); and U.S. ProvisionalPatent Application Ser. No. 62/583,487, filed Nov. 8, 2017, entitledMETHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS(STRF-0001-P07).

The present application claims the benefit of, and is a bypasscontinuation of, International Application Number PCT/US18/45036, filedAug. 2, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN ANINDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGEDATA SETS (STRF-0011-WO).

International Application Number PCT/US18/45036 (STRF-0011-WO) claimsthe benefit of, and is a continuation of, U.S. Non-Provisional patentapplication Ser. No. 15/973,406, filed May 7, 2018, entitled METHODS ANDSYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATACOLLECTION ENVIRONMENT WITH LARGE DATA SETS (STRF-0001-U22).

International Application Number PCT/US18/45036 (STRF-0011-WO) claimspriority to: U.S. Provisional Patent Application Ser. No. 62/540,557,filed Aug. 2, 2017, entitled SMART HEATING SYSTEMS IN AN INDUSTRIALINTERNET OF THINGS (STRF-0001-P05); U.S. Provisional Patent ApplicationSer. No. 62/540,513, filed Aug. 2, 2017, entitled SYSTEMS AND METHODSFOR SMART HEATING SYSTEM THAT PRODUCES AND USES HYDROGEN FUEL(STRF-0001-P08); U.S. Provisional Patent Application Ser. No.62/562,487, filed Sep. 24, 2017, entitled METHODS AND SYSTEMS FOR THEINDUSTRIAL INTERNET OF THINGS (STRF-0001-P06); and U.S. ProvisionalPatent Application Ser. No. 62/583,487, filed Nov. 8, 2017, entitledMETHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS(STRF-0001-P07).

The present application claims priority to U.S. Provisional PatentApplication Ser. No. 62/583,487, filed Nov. 8, 2017, entitled METHODSAND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS (STRF-0001-P07).

All of the foregoing applications are hereby incorporated by referenceas if fully set forth herein in their entirety.

BACKGROUND 1. Field

The present disclosure relates to methods and systems for datacollection in industrial environments, as well as methods and systemsfor leveraging collected data for monitoring, remote control, autonomousaction, and other activities in industrial environments.

2. Description of the Related Art

Heavy industrial environments, such as environments for large scalemanufacturing (such as manufacturing of aircraft, ships, trucks,automobiles, and large industrial machines), energy productionenvironments (such as oil and gas plants, renewable energy environments,and others), energy extraction environments (such as mining, drilling,and the like), construction environments (such as for construction oflarge buildings), and others, involve highly complex machines, devicesand systems and highly complex workflows, in which operators mustaccount for a host of parameters, metrics, and the like in order tooptimize design, development, deployment, and operation of differenttechnologies in order to improve overall results. Historically, data hasbeen collected in heavy industrial environments by human beings usingdedicated data collectors, often recording batches of specific sensordata on media, such as tape or a hard drive, for later analysis. Batchesof data have historically been returned to a central office foranalysis, such as undertaking signal processing or other analysis on thedata collected by various sensors, after which analysis can be used as abasis for diagnosing problems in an environment and/or suggesting waysto improve operations. This work has historically taken place on a timescale of weeks or months, and has been directed to limited data sets.

The emergence of the Internet of Things (IoT) has made it possible toconnect continuously to, and among, a much wider range of devices. Mostsuch devices are consumer devices, such as lights, thermostats, and thelike. More complex industrial environments remain more difficult, as therange of available data is often limited, and the complexity of dealingwith data from multiple sensors makes it much more difficult to produce“smart” solutions that are effective for the industrial sector. A needexists for improved methods and systems for data collection inindustrial environments, as well as for improved methods and systems forusing collected data to provide improved monitoring, control,intelligent diagnosis of problems and intelligent optimization ofoperations in various heavy industrial environments.

Industrial system in various environments have a number of challenges toutilizing data from a multiplicity of sensors. Many industrial systemshave a wide range of computing resources and network capabilities at alocation at a given time, for example as parts of the system areupgraded or replaced on varying time scales, as mobile equipment entersor leaves a location, and due to the capital costs and risks ofupgrading equipment. Additionally, many industrial systems arepositioned in challenging environments, where network connectivity canbe variable, where a number of noise sources such as vibrational noiseand electro-magnetic (EM) noise sources can be significant an in variedlocations, and with portions of the system having high pressure, highnoise, high temperature, and corrosive materials. Many industrialprocesses are subject to high variability in process operatingparameters and non-linear responses to off-nominal operations.Accordingly, sensing requirements for industrial processes can vary withtime, operating stages of a process, age and degradation of equipment,and operating conditions. Previously known industrial processes sufferfrom sensing configurations that are conservative, detecting manyparameters that are not needed during most operations of the industrialsystem, or that accept risk in the process, and do not detect parametersthat are only occasionally utilized in characterizing the system.Further, previously known industrial systems are not flexible toconfiguring sensed parameters rapidly and in real-time, and in managingsystem variance such as intermittent network availability. Industrialsystems often use similar components across systems such as pumps,mixers, tanks, and fans. However, previously known industrial systems donot have a mechanism to leverage data from similar components that maybe used in a different type of process, and/or that may be unavailabledue to competitive concerns. Additionally, previously known industrialsystems do not integrate data from offset systems into the sensor planand execution in real time.

SUMMARY

In an aspect, systems for monitoring data collection in an industrialenvironment may include a data storage structured to store detectionparameters for a plurality of input channels; a data collectorcommunicatively coupled to the plurality of input channels, wherein thedata collector collects data from the plurality of input channels basedon the detection parameters; a data acquisition circuit structured tointerpret a plurality of detection values from the collected data, eachof the plurality of detection values corresponding to at least one ofthe plurality of input channels; and a data analysis circuit structuredto analyze the collected data from the plurality of input channels,wherein one of the plurality of input channels is connected to avibration sensor providing continuous vibration data, wherein when thedata analysis circuit detects a measured vibration data value outside apredetermined range for the continuous vibration data, the datacollector switches from a first detection parameter to a seconddetection parameter for data collection from the one of the plurality ofinput channels connected to the vibration sensor. In embodiments, thevibration sensor may be a tri-axial sensor connected to multiple inputchannels. The vibration sensor may generate a gap-free digital waveformfrom which the data analysis circuit detects an anomalous condition. Thedata analysis circuit may analyze a first and a second of the pluralityof input channels connected to vibration sensors for a relative phasedetermination from which the data analysis circuit detects the measuredvibration data value outside the predetermined range for the continuousvibration data. The data analysis circuit may analyze frequencycomponents in detecting the measured vibration data value outside apredetermined range. The data analysis circuit may analyze a signalcondition for signal-to-noise in detecting the measured vibration datavalue outside a predetermined range. The data collector may switch froma first detection parameter to a second detection parameter by modifyinga data collection trajectory by changing a vibration sensor monitoringlocation. The data collector may switched from a first detectionparameter to a second detection parameter by modifying a data collectiontrajectory by changing a vibration sensor capability.

In an aspect, a computer-implemented method for data collection in anindustrial environment may include accessing stored detection parametersin a data storage for a plurality of input channels; collecting datafrom a plurality of input channels communicatively coupled to a datacollector, wherein the collecting data is based on the detectionparameters; interpreting a plurality of detection values from thecollected data by a data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the plurality of inputchannels; and analyzing the collected data from the plurality of inputchannels, wherein one of the plurality of input channels is connected toa vibration sensor providing continuous vibration data, wherein theanalyzing comprises detecting a measured vibration data value outside apredetermined range for the continuous vibration data, and switchingfrom a first detection parameter to a second detection parameter fordata collection from the one of the plurality of input channelsconnected to the vibration sensor. In embodiments, the vibration sensormay generate a gap-free digital waveform from which the data analysiscircuit detects an anomalous condition. The analyzing may furtherinclude determining a relative phase difference between a first and asecond of the plurality of input channels, each connected to vibrationsensors, and wherein the detecting the measured vibration data valuesoutside the predetermined range is in response to the relative phasedifference. The analyzing may further include determining frequencycomponents of the continuous vibration data, and wherein the detectingthe measured vibration data values outside the predetermined range is inresponse to the frequency components. The analyzing may further includeperforming a signal conditioning to improve signal-to-noise ratio in thecontinuous vibration data. The performing the signal conditioning mayfurther include at least one of filtering the continuous vibration dataand anti-aliasing the continuous vibration data. The data collector mayinclude a multiplexer, and wherein the performing the signalconditioning is performed before switching of the multiplexer.

In an aspect, an apparatus for monitoring data collection in anindustrial environment may include a data storage structured to storedetection parameters for a plurality of input channels; a data collectorcommunicatively coupled to the plurality of input channels, wherein thedata collector collects data from the plurality of input channels basedon the detection parameters; a data acquisition circuit structured tointerpret a plurality of detection values from the collected data, eachof the plurality of detection values corresponding to at least one ofthe plurality of input channels; and a data analysis circuit structuredto analyze the collected data from the plurality of input channels,wherein a first one of the plurality of input channels is connected to atri-axial vibration sensor, wherein a second one of the plurality ofinput channels is connected to a single axis vibration sensor, andwherein the tri-axial and single axis vibration sensors providecontinuous vibration data, wherein when the data analysis circuitdetects a measured vibration data value outside a predetermined rangefor the continuous vibration data, the data collector switches from afirst detection parameter to a second detection parameter for datacollection from the one of the plurality of input channels, wherein thefirst detection parameter comprises one of a high sampling speed or alow sampling speed, and wherein the second detection parameter comprisesthe other of the high sampling speed or the low sampling speed. Inembodiments, the data collector may be structured to use interpolationto increase the effective sampling speed of at least one of the inputchannels. The data collector may be further structured to use decimationto decrease the effective sampling speed of at least one of the inputchannels. The data analysis circuit may analyze the first and the secondof the plurality of input channels to determine a relative phasedifference and detects the measured vibration data value outside thepredetermined range for the continuous vibration data in response to therelative phase difference. The data analysis circuit may perform asignal conditioning operation to improve a signal-to-noise ratio in thecontinuous vibration data.

Methods and systems are provided herein for data collection inindustrial environments, as well as for improved methods and systems forusing collected data to provide improved monitoring, control, andintelligent diagnosis of problems and intelligent optimization ofoperations in various heavy industrial environments. These methods andsystems include methods, systems, components, devices, workflows,services, processes, and the like that are deployed in variousconfigurations and locations, such as: (a) at the “edge” of the Internetof Things, such as in the local environment of a heavy industrialmachine; (b) in data transport networks that move data between localenvironments of heavy industrial machines and other environments, suchas of other machines or of remote controllers, such as enterprises thatown or operate the machines or the facilities in which the machines areoperated; and (c) in locations where facilities are deployed to controlmachines or their environments, such as cloud-computing environments andon-premises computing environments of enterprises that own or controlheavy industrial environments or the machines, devices or systemsdeployed in them. These methods and systems include a range of ways forproviding improved data include a range of methods and systems forproviding improved data collection, as well as methods and systems fordeploying increased intelligence at the edge, in the network, and in thecloud or premises of the controller of an industrial environment.

Methods and systems are disclosed herein for continuous ultrasonicmonitoring, including providing continuous ultrasonic monitoring ofrotating elements and bearings of an energy production facility; forcloud-based systems including machine pattern recognition based on thefusion of remote, analog industrial sensors or machine pattern analysisof state information from multiple analog industrial sensors to provideanticipated state information for an industrial system; for on-devicesensor fusion and data storage for industrial IoT devices, includingon-device sensor fusion and data storage for an Industrial IoT device,where data from multiple sensors are multiplexed at the device forstorage of a fused data stream; and for self-organizing systemsincluding a self-organizing data marketplace for industrial IoT data,including a self-organizing data marketplace for industrial IoT data,where available data elements are organized in the marketplace forconsumption by consumers based on training a self-organizing facilitywith a training set and feedback from measures of marketplace success,for self-organizing data pools, including self-organization of datapools based on utilization and/or yield metrics, including utilizationand/or yield metrics that are tracked for a plurality of data pools, aself-organized swarm of industrial data collectors, including aself-organizing swarm of industrial data collectors that organize amongthemselves to optimize data collection based on the capabilities andconditions of the members of the swarm, a self-organizing collector,including a self-organizing, multi-sensor data collector that canoptimize data collection, power and/or yield based on conditions in itsenvironment, a self-organizing storage for a multi-sensor datacollector, including self-organizing storage for a multi-sensor datacollector for industrial sensor data, a self-organizing network codingfor a multi-sensor data network, including self-organizing networkcoding for a data network that transports data from multiple sensors inan industrial data collection environment.

Methods and systems are disclosed herein for training artificialintelligence (“AI”) models based on industry-specific feedback,including training an AI model based on industry-specific feedback thatreflects a measure of utilization, yield, or impact, where the AI modeloperates on sensor data from an industrial environment; for anindustrial IoT distributed ledger, including a distributed ledgersupporting the tracking of transactions executed in an automated datamarketplace for industrial IoT data; for a network-sensitive collector,including a network condition-sensitive, self-organizing, multi-sensordata collector that can optimize based on bandwidth, quality of service,pricing, and/or other network conditions; for a remotely organizeduniversal data collector that can power up and down sensor interfacesbased on need and/or conditions identified in an industrial datacollection environment; and for a haptic or multi-sensory userinterface, including a wearable haptic or multi-sensory user interfacefor an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs.

Methods and systems are disclosed herein for a presentation layer foraugmented reality and virtual reality (AR/VR) industrial glasses, whereheat map elements are presented based on patterns and/or parameters incollected data; and for condition-sensitive, self-organized tuning ofAR/VR interfaces based on feedback metrics and/or training in industrialenvironments.

In embodiments, a system for data collection, processing, andutilization of signals from at least a first element in a first machinein an industrial environment includes a platform including a computingenvironment connected to a local data collection system having at leasta first sensor signal and a second sensor signal obtained from at leastthe first machine in the industrial environment. The system includes afirst sensor in the local data collection system configured to beconnected to the first machine and a second sensor in the local datacollection system. The system further includes a crosspoint switch inthe local data collection system having multiple inputs and multipleoutputs including a first input connected to the first sensor and asecond input connected to the second sensor. Throughout the presentdisclosure, wherever a crosspoint switch, multiplexer (MUX) device, orother multiple-input multiple-output data collection or communicationdevice is described, any multi-sensor acquisition device is alsocontemplated herein. In certain embodiments, a multi-sensor acquisitiondevice includes one or more channels configured for, or compatible with,an analog sensor input. The multiple outputs include a first output andsecond output configured to be switchable between a condition in whichthe first output is configured to switch between delivery of the firstsensor signal and the second sensor signal and a condition in whichthere is simultaneous delivery of the first sensor signal from the firstoutput and the second sensor signal from the second output. Each ofmultiple inputs is configured to be individually assigned to any of themultiple outputs, or combined in any subsets of the inputs to theoutputs. Unassigned outputs are configured to be switched off, forexample by producing a high-impedance state.

In embodiments, the first sensor signal and the second sensor signal arecontinuous vibration data about the industrial environment. Inembodiments, the second sensor in the local data collection system isconfigured to be connected to the first machine. In embodiments, thesecond sensor in the local data collection system is configured to beconnected to a second machine in the industrial environment. Inembodiments, the computing environment of the platform is configured tocompare relative phases of the first and second sensor signals. Inembodiments, the first sensor is a single-axis sensor and the secondsensor is a three-axis sensor. In embodiments, at least one of themultiple inputs of the crosspoint switch includes internet protocol,front-end signal conditioning, for improved signal-to-noise ratio. Inembodiments, the crosspoint switch includes a third input that isconfigured with a continuously monitored alarm having a pre-determinedtrigger condition when the third input is unassigned to or undetected atany of the multiple outputs.

In embodiments, the local data collection system includes multiplemultiplexing units and multiple data acquisition units receivingmultiple data streams from multiple machines in the industrialenvironment. In embodiments, the local data collection system includesdistributed complex programmable hardware device (“CPLD”) chips eachdedicated to a data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units that receive the multipledata streams from the multiple machines in the industrial environment.In embodiments, the local data collection system is configured toprovide high-amperage input capability using solid state relays. Inembodiments, the local data collection system is configured topower-down at least one of an analog sensor channel and a componentboard.

In embodiments, the local data collection system includes a phase-lockloop band-pass tracking filter configured to obtain slow-speedrevolutions per minute (“RPMs”) and phase information. In embodiments,the local data collection system is configured to digitally derive phaseusing on-board timers relative to at least one trigger channel and atleast one of the multiple inputs. In embodiments, the local datacollection system includes a peak-detector configured to autoscale usinga separate analog-to-digital converter for peak detection. Inembodiments, the local data collection system is configured to route atleast one trigger channel that is raw and buffered into at least one ofthe multiple inputs. In embodiments, the local data collection systemincludes at least one delta-sigma analog-to-digital converter that isconfigured to increase input oversampling rates to reduce sampling rateoutputs and to minimize anti-aliasing filter requirements. Inembodiments, the distributed CPLD chips each dedicated to the data busfor logic control of the multiple multiplexing units and the multipledata acquisition units includes as high-frequency crystal clockreference configured to be divided by at least one of the distributedCPLD chips for at least one delta-sigma analog-to-digital converter toachieve lower sampling rates without digital resampling.

In embodiments, the local data collection system is configured to obtainlong blocks of data at a single relatively high-sampling rate as opposedto multiple sets of data taken at different sampling rates. Inembodiments, the single relatively high-sampling rate corresponds to amaximum frequency of about forty kilohertz. In embodiments, the longblocks of data are for a duration that is in excess of one minute. Inembodiments, the local data collection system includes multiple dataacquisition units each having an onboard card set configured to storecalibration information and maintenance history of a data acquisitionunit in which the onboard card set is located. In embodiments, the localdata collection system is configured to plan data acquisition routesbased on hierarchical templates.

In embodiments, the local data collection system is configured to managedata collection bands. In embodiments, the data collection bands definea specific frequency band and at least one of a group of spectral peaks,a true-peak level, a crest factor derived from a time waveform, and anoverall waveform derived from a vibration envelope. In embodiments, thelocal data collection system includes a neural net expert system usingintelligent management of the data collection bands. In embodiments, thelocal data collection system is configured to create data acquisitionroutes based on hierarchical templates that each include the datacollection bands related to machines associated with the dataacquisition routes. In embodiments, at least one of the hierarchicaltemplates is associated with multiple interconnected elements of thefirst machine. In embodiments, at least one of the hierarchicaltemplates is associated with similar elements associated with at leastthe first machine and a second machine. In embodiments, at least one ofthe hierarchical templates is associated with at least the first machinebeing proximate in location to a second machine.

In embodiments, the local data collection system includes a graphicaluser interface (“GUI”) system configured to manage the data collectionbands. In embodiments, the GUI system includes an expert systemdiagnostic tool. In embodiments, the platform includes cloud-based,machine pattern analysis of state information from multiple sensors toprovide anticipated state information for the industrial environment. Inembodiments, the platform is configured to provide self-organization ofdata pools based on at least one of the utilization metrics and yieldmetrics. In embodiments, the platform includes a self-organized swarm ofindustrial data collectors. In embodiments, the local data collectionsystem includes a wearable haptic user interface for an industrialsensor data collector with at least one of vibration, heat, electrical,and sound outputs.

In embodiments, multiple inputs of the crosspoint switch include a thirdinput connected to the second sensor and a fourth input connected to thesecond sensor. The first sensor signal is from a single-axis sensor atan unchanging location associated with the first machine. Inembodiments, the second sensor is a three-axis sensor. In embodiments,the local data collection system is configured to record gap-freedigital waveform data simultaneously from at least the first input, thesecond input, the third input, and the fourth input. In embodiments, theplatform is configured to determine a change in relative phase based onthe simultaneously recorded gap-free digital waveform data. Inembodiments, the second sensor is configured to be movable to aplurality of positions associated with the first machine while obtainingthe simultaneously recorded gap-free digital waveform data. Inembodiments, multiple outputs of the crosspoint switch include a thirdoutput and fourth output. The second, third, and fourth outputs areassigned together to a sequence of tri-axial sensors each located atdifferent positions associated with the machine. In embodiments, theplatform is configured to determine an operating deflection shape basedon the change in relative phase and the simultaneously recorded gap-freedigital waveform data.

In embodiments, the unchanging location is a position associated withthe rotating shaft of the first machine. In embodiments, tri-axialsensors in the sequence of the tri-axial sensors are each located atdifferent positions on the first machine but are each associated withdifferent bearings in the machine. In embodiments, tri-axial sensors inthe sequence of the tri-axial sensors are each located at similarpositions associated with similar bearings but are each associated withdifferent machines. In embodiments, the local data collection system isconfigured to obtain the simultaneously recorded gap-free digitalwaveform data from the first machine while the first machine and asecond machine are both in operation. In embodiments, the local datacollection system is configured to characterize a contribution from thefirst machine and the second machine in the simultaneously recordedgap-free digital waveform data from the first machine. In embodiments,the simultaneously recorded gap-free digital waveform data has aduration that is in excess of one minute.

In embodiments, a method of monitoring a machine having at least oneshaft supported by a set of bearings includes monitoring a first datachannel assigned to a single-axis sensor at an unchanging locationassociated with the machine. The method includes monitoring second,third, and fourth data channels each assigned to an axis of a three-axissensor. The method includes recording gap-free digital waveform datasimultaneously from all of the data channels while the machine is inoperation and determining a change in relative phase based on thedigital waveform data.

In embodiments, the tri-axial sensor is located at a plurality ofpositions associated with the machine while obtaining the digitalwaveform. In embodiments, the second, third, and fourth channels areassigned together to a sequence of tri-axial sensors each located atdifferent positions associated with the machine. In embodiments, thedata is received from all of the sensors simultaneously. In embodiments,the method includes determining an operating deflection shape based onthe change in relative phase information and the waveform data. Inembodiments, the unchanging location is a position associated with theshaft of the machine. In embodiments, the tri-axial sensors in thesequence of the tri-axial sensors are each located at differentpositions and are each associated with different bearings in themachine. In embodiments, the unchanging location is a positionassociated with the shaft of the machine. The tri-axial sensors in thesequence of the tri-axial sensors are each located at differentpositions and are each associated with different bearings that supportthe shaft in the machine.

In embodiments, the method includes monitoring the first data channelassigned to the single-axis sensor at an unchanging location located ona second machine. The method includes monitoring the second, the third,and the fourth data channels, each assigned to the axis of a three-axissensor that is located at the position associated with the secondmachine. The method also includes recording gap-free digital waveformdata simultaneously from all of the data channels from the secondmachine while both of the machines are in operation. In embodiments, themethod includes characterizing the contribution from each of themachines in the gap-free digital waveform data simultaneously from thesecond machine.

In embodiments, a method for data collection, processing, andutilization of signals with a platform monitoring at least a firstelement in a first machine in an industrial environment includesobtaining, automatically with a computing environment, at least a firstsensor signal and a second sensor signal with a local data collectionsystem that monitors at least the first machine. The method includesconnecting a first input of a crosspoint switch of the local datacollection system to a first sensor and a second input of the crosspointswitch to a second sensor in the local data collection system. Themethod includes switching between a condition in which a first output ofthe crosspoint switch alternates between delivery of at least the firstsensor signal and the second sensor signal and a condition in whichthere is simultaneous delivery of the first sensor signal from the firstoutput and the second sensor signal from a second output of thecrosspoint switch. The method also includes switching off unassignedoutputs of the crosspoint switch into a high-impedance state.

In embodiments, the first sensor signal and the second sensor signal arecontinuous vibration data from the industrial environment. Inembodiments, the second sensor in the local data collection system isconnected to the first machine. In embodiments, the second sensor in thelocal data collection system is connected to a second machine in theindustrial environment. In embodiments, the method includes comparing,automatically with the computing environment, relative phases of thefirst and second sensor signals. In embodiments, the first sensor is asingle-axis sensor and the second sensor is a three-axis sensor. Inembodiments, at least the first input of the crosspoint switch includesinternet protocol front-end signal conditioning for improvedsignal-to-noise ratio.

In embodiments, the method includes continuously monitoring at least athird input of the crosspoint switch with an alarm having apre-determined trigger condition when the third input is unassigned toany of multiple outputs on the crosspoint switch. In embodiments, thelocal data collection system includes multiple multiplexing units andmultiple data acquisition units receiving multiple data streams frommultiple machines in the industrial environment. In embodiments, thelocal data collection system includes distributed CPLD chips eachdedicated to a data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units that receive the multipledata streams from the multiple machines in the industrial environment.In embodiments, the local data collection system provides high-amperageinput capability using solid state relays.

In embodiments, the method includes powering down at least one of ananalog sensor channel and a component board of the local data collectionsystem. In embodiments, the local data collection system includes anexternal voltage reference for an A/D zero reference that is independentof the voltage of the first sensor and the second sensor. Inembodiments, the local data collection system includes a phase-lock loopband-pass tracking filter that obtains slow-speed RPMs and phaseinformation. In embodiments, the method includes digitally derivingphase using on-board timers relative to at least one trigger channel andat least one of multiple inputs on the crosspoint switch.

In embodiments, the method includes auto-scaling with a peak-detectorusing a separate analog-to-digital converter for peak detection. Inembodiments, the method includes routing at least one trigger channelthat is raw and buffered into at least one of multiple inputs on thecrosspoint switch. In embodiments, the method includes increasing inputoversampling rates with at least one delta-sigma analog-to-digitalconverter to reduce sampling rate outputs and to minimize anti-aliasingfilter requirements. In embodiments, the distributed CPLD chips are eachdedicated to the data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units and each include ahigh-frequency crystal clock reference divided by at least one of thedistributed CPLD chips for at least one delta-sigma analog-to-digitalconverter to achieve lower sampling rates without digital resampling. Inembodiments, the method includes obtaining long blocks of data at asingle relatively high-sampling rate with the local data collectionsystem as opposed to multiple sets of data taken at different samplingrates. In embodiments, the single relatively high-sampling ratecorresponds to a maximum frequency of about forty kilohertz. Inembodiments, the long blocks of data are for a duration that is inexcess of one minute. In embodiments, the local data collection systemincludes multiple data acquisition units and each data acquisition unithas an onboard card set that stores calibration information andmaintenance history of a data acquisition unit in which the onboard cardset is located.

In embodiments, the method includes planning data acquisition routesbased on hierarchical templates associated with at least the firstelement in the first machine in the industrial environment. Inembodiments, the local data collection system manages data collectionbands that define a specific frequency band and at least one of a groupof spectral peaks, a true-peak level, a crest factor derived from a timewaveform, and an overall waveform derived from a vibration envelope. Inembodiments, the local data collection system includes a neural netexpert system using intelligent management of the data collection bands.In embodiments, the local data collection system creates dataacquisition routes based on hierarchical templates that each include thedata collection bands related to machines associated with the dataacquisition routes. In embodiments, at least one of the hierarchicaltemplates is associated with multiple interconnected elements of thefirst machine. In embodiments, at least one of the hierarchicaltemplates is associated with similar elements associated with at leastthe first machine and a second machine. In embodiments, at least one ofthe hierarchical templates is associated with at least the first machinebeing proximate in location to a second machine.

In embodiments, the method includes controlling a GUI system of thelocal data collection system to manage the data collection bands. TheGUI system includes an expert system diagnostic tool. In embodiments,the computing environment of the platform includes cloud-based, machinepattern analysis of state information from multiple sensors to provideanticipated state information for the industrial environment. Inembodiments, the computing environment of the platform providesself-organization of data pools based on at least one of the utilizationmetrics and yield metrics. In embodiments, the computing environment ofthe platform includes a self-organized swarm of industrial datacollectors. In embodiments, each of multiple inputs of the crosspointswitch is individually assignable to any of multiple outputs of thecrosspoint switch.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for capturing a plurality ofstreams of sensed data from sensors deployed to monitor aspects of anindustrial machine associated with at least one moving part of themachine; at least one of the streams contains a plurality of frequenciesof data. The method may include identifying a subset of data in at leastone of the plurality of streams that corresponds to data representing atleast one predefined frequency. The at least one predefined frequency isrepresented by a set of data collected from alternate sensors deployedto monitor aspects of the industrial machine associated with the atleast one moving part of the machine. The method may further includeprocessing the identified data with a data processing facility thatprocesses the identified data with an algorithm configured to be appliedto the set of data collected from alternate sensors. Lastly, the methodmay include storing the at least one of the streams of data, theidentified subset of data, and a result of processing the identifieddata in an electronic data set.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing, andstorage systems and may include a method for applying data captured fromsensors deployed to monitor aspects of an industrial machine associatedwith at least one moving part of the machine. The data is captured withpredefined lines of resolution covering a predefined frequency range andis sent to a frequency matching facility that identifies a subset ofdata streamed from other sensors deployed to monitor aspects of theindustrial machine associated with at least one moving part of themachine. The streamed data includes a plurality of lines of resolutionand frequency ranges. The subset of data identified corresponds to thelines of resolution and predefined frequency range. This method mayinclude storing the subset of data in an electronic data record in aformat that corresponds to a format of the data captured with predefinedlines of resolution and signaling to a data processing facility thepresence of the stored subset of data. This method may, optionally,include processing the subset of data with at least one set ofalgorithms, models and pattern recognizers that corresponds toalgorithms, models and pattern recognizers associated with processingthe data captured with predefined lines of resolution covering apredefined frequency range.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for identifying a subset ofstreamed sensor data, the sensor data captured from sensors deployed tomonitor aspects of an industrial machine associated with at least onemoving part of the machine, the subset of streamed sensor data atpredefined lines of resolution for a predefined frequency range, andestablishing a first logical route for communicating electronicallybetween a first computing facility performing the identifying and asecond computing facility, wherein identified subset of the streamedsensor data is communicated exclusively over the established firstlogical route when communicating the subset of streamed sensor data fromthe first facility to the second facility. This method may furtherinclude establishing a second logical route for communicatingelectronically between the first computing facility and the secondcomputing facility for at least one portion of the streamed sensor datathat is not the identified subset. Additionally, this method may furtherinclude establishing a third logical route for communicatingelectronically between the first computing facility and the secondcomputing facility for at least one portion of the streamed sensor datathat includes the identified subset and at least one other portion ofthe data not represented by the identified subset.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a first data sensing and processingsystem that captures first data from a first set of sensors deployed tomonitor aspects of an industrial machine associated with at least onemoving part of the machine, the first data covering a set of lines ofresolution and a frequency range. This system may include a second datasensing and processing system that captures and streams a second set ofdata from a second set of sensors deployed to monitor aspects of theindustrial machine associated with at least one moving part of themachine, the second data covering a plurality of lines of resolutionthat includes the set of lines of resolution and a plurality offrequencies that includes the frequency range. The system may enableselecting a portion of the second data that corresponds to the set oflines of resolution and the frequency range of the first data, andprocessing the selected portion of the second data with the first datasensing and processing system.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for automatically processing aportion of a stream of sensed data. The sensed data is received from afirst set of sensors deployed to monitor aspects of an industrialmachine associated with at least one moving part of the machine. Thesensed data is in response to an electronic data structure thatfacilitates extracting a subset of the stream of sensed data thatcorresponds to a set of sensed data received from a second set ofsensors deployed to monitor the aspects of the industrial machineassociated with the at least one moving part of the machine. The set ofsensed data is constrained to a frequency range. The stream of senseddata includes a range of frequencies that exceeds the frequency range ofthe set of sensed data, the processing comprising executing an algorithmon a portion of the stream of sensed data that is constrained to thefrequency range of the set of sensed data, the algorithm configured toprocess the set of sensed data.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for receiving first data fromsensors deployed to monitor aspects of an industrial machine associatedwith at least one moving part of the machine. This method may furtherinclude detecting at least one of a frequency range and lines ofresolution represented by the first data; receiving a stream of datafrom sensors deployed to monitor the aspects of the industrial machineassociated with the at least one moving part of the machine. The streamof data includes: (1) a plurality of frequency ranges and a plurality oflines of resolution that exceeds the frequency range and the lines ofresolution represented by the first data; (2) a set of data extractedfrom the stream of data that corresponds to at least one of thefrequency range and the lines of resolution represented by the firstdata; and (3) the extracted set of data which is processed with a dataprocessing algorithm that is configured to process data within thefrequency range and within the lines of resolution of the first data.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 through FIG. 5 are diagrammatic views that each depicts portionsof an overall view of an industrial Internet of Things (IoT) datacollection, monitoring and control system in accordance with the presentdisclosure.

FIG. 6 is a diagrammatic view of a platform including a local datacollection system disposed in an industrial environment for collectingdata from or about the elements of the environment, such as machines,components, systems, sub-systems, ambient conditions, states, workflows,processes, and other elements in accordance with the present disclosure.

FIG. 7 is a diagrammatic view that depicts elements of an industrialdata collection system for collecting analog sensor data in anindustrial environment in accordance with the present disclosure.

FIG. 8 is a diagrammatic view of a rotating or oscillating machinehaving a data acquisition module that is configured to collect waveformdata in accordance with the present disclosure.

FIG. 9 is a diagrammatic view of an exemplary tri-axial sensor mountedto a motor bearing of an exemplary rotating machine in accordance withthe present disclosure.

FIG. 10 and FIG. 11 are diagrammatic views of an exemplary tri-axialsensor and a single-axis sensor mounted to an exemplary rotating machinein accordance with the present disclosure.

FIG. 12 is a diagrammatic view of multiple machines under survey withensembles of sensors in accordance with the present disclosure.

FIG. 13 is a diagrammatic view of hybrid relational metadata and abinary storage approach in accordance with the present disclosure.

FIG. 14 is a diagrammatic view of components and interactions of a datacollection architecture involving application of cognitive and machinelearning systems to data collection and processing in accordance withthe present disclosure.

FIG. 15 is a diagrammatic view of components and interactions of a datacollection architecture involving application of a platform having acognitive data marketplace in accordance with the present disclosure.

FIG. 16 is a diagrammatic view of components and interactions of a datacollection architecture involving application of a self-organizing swarmof data collectors in accordance with the present disclosure.

FIG. 17 is a diagrammatic view of components and interactions of a datacollection architecture involving application of a haptic user interfacein accordance with the present disclosure.

FIG. 18 is a diagrammatic view of a multi-format streaming datacollection system in accordance with the present disclosure.

FIG. 19 is a diagrammatic view of combining legacy and streaming datacollection and storage in accordance with the present disclosure.

FIG. 20 is a diagrammatic view of industrial machine sensing using bothlegacy and updated streamed sensor data processing in accordance withthe present disclosure.

FIG. 21 is a diagrammatic view of an industrial machine sensed dataprocessing system that facilitates portal algorithm use and alignment oflegacy and streamed sensor data in accordance with the presentdisclosure.

FIG. 22 is a diagrammatic view of components and interactions of a datacollection architecture involving a streaming data acquisitioninstrument receiving analog sensor signals from an industrialenvironment connected to a cloud network facility in accordance with thepresent disclosure.

FIG. 23 is a diagrammatic view of components and interactions of a datacollection architecture involving a streaming data acquisitioninstrument having an alarms module, expert analysis module, and a driverAPI to facilitate communication with a cloud network facility inaccordance with the present disclosure.

FIG. 24 is a diagrammatic view of components and interactions of a datacollection architecture involving a streaming data acquisitioninstrument and first in, first out memory architecture to provide a realtime operating system in accordance with the present disclosure.

FIG. 25 through FIG. 30 are diagrammatic views of screens showing fouranalog sensor signals, transfer functions between the signals, analysisof each signal, and operating controls to move and edit throughout thestreaming signals obtained from the sensors in accordance with thepresent disclosure.

FIG. 31 is a diagrammatic view of components and interactions of a datacollection architecture involving a multiple streaming data acquisitioninstrument receiving analog sensor signals and digitizing those signalsto be obtained by a streaming hub server in accordance with the presentdisclosure.

FIG. 32 is a diagrammatic view of components and interactions of a datacollection architecture involving a master raw data server thatprocesses new streaming data and data already extracted and processed inaccordance with the present disclosure.

FIG. 33, FIG. 34, and FIG. 35 are diagrammatic views of components andinteractions of a data collection architecture involving a processing,analysis, report, and archiving server that processes new streaming dataand data already extracted and processed in accordance with the presentdisclosure.

FIG. 36 is a diagrammatic view of components and interactions of a datacollection architecture involving a relation database server and dataarchives and their connectivity with a cloud network facility inaccordance with the present disclosure.

FIG. 37 through FIG. 42 are diagrammatic views of components andinteractions of a data collection architecture involving a virtualstreaming data acquisition instrument receiving analog sensor signalsfrom an industrial environment connected to a cloud network facility inaccordance with the present disclosure.

FIG. 43 through FIG. 49 are diagrammatic views of components andinteractions of a data collection architecture involving data channelmethods and systems for data collection of industrial machines inaccordance with the present disclosure.

FIG. 50 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 51 and FIG. 52 are diagrammatic views that depict embodiments of adata monitoring device in accordance with the present disclosure.

FIG. 53 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 54 and 55 are diagrammatic views that depict an embodiment of asystem for data collection in accordance with the present disclosure.

FIGS. 56 and 57 are diagrammatic views that depict an embodiment of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 58 depicts an embodiment of a data monitoring device incorporatingsensors in accordance with the present disclosure.

FIGS. 59 and 60 are diagrammatic views that depict embodiments of a datamonitoring device in communication with external sensors in accordancewith the present disclosure.

FIG. 61 is a diagrammatic view that depicts embodiments of a datamonitoring device with additional detail in the signal evaluationcircuit in accordance with the present disclosure.

FIG. 62 is a diagrammatic view that depicts embodiments of a datamonitoring device with additional detail in the signal evaluationcircuit in accordance with the present disclosure.

FIG. 63 is a diagrammatic view that depicts embodiments of a datamonitoring device with additional detail in the signal evaluationcircuit in accordance with the present disclosure.

FIG. 64 is a diagrammatic view that depicts embodiments of a system fordata collection in accordance with the present disclosure.

FIG. 65 is a diagrammatic view that depicts embodiments of a system fordata collection comprising a plurality of data monitoring devices inaccordance with the present disclosure.

FIG. 66 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 67 and 68 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 69 and 70 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 71 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 72 and 73 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 74 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 75 and 76 are diagrammatic views that depict embodiments of asystem for data collection in accordance with the present disclosure.

FIGS. 77 and 78 are diagrammatic views that depict embodiments of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 79 to FIG. 106 are diagrammatic views of components andinteractions of a data collection architecture involving various neuralnetwork embodiments interacting with a streaming data acquisitioninstrument receiving analog sensor signals and an expert analysis modulein accordance with the present disclosure.

FIG. 107 through FIG. 109 are diagrammatic views of components andinteractions of a data collection architecture involving a collector ofroute templates and the routing of data collectors in an industrialenvironment in accordance with the present disclosure.

FIG. 110 is a diagrammatic view that depicts a monitoring system thatemploys data collection bands in accordance with the present disclosure.

FIG. 111 is a diagrammatic view that depicts a system that employsvibration and other noise in predicting states and outcomes inaccordance with the present disclosure.

FIG. 112 is a diagrammatic view that depicts a system for datacollection in an industrial environment in accordance with the presentdisclosure.

FIG. 113 is a diagrammatic view that depicts an apparatus for datacollection in an industrial environment in accordance with the presentdisclosure.

FIG. 114 is a schematic flow diagram of a procedure for data collectionin an industrial environment in accordance with the present disclosure.

FIG. 115 is a diagrammatic view that depicts a system for datacollection in an industrial environment in accordance with the presentdisclosure.

FIG. 116 is a diagrammatic view that depicts an apparatus for datacollection in an industrial environment in accordance with the presentdisclosure.

FIG. 117 is a schematic flow diagram of a procedure for data collectionin an industrial environment in accordance with the present disclosure.

FIG. 118 is a diagrammatic view that depicts industry-specific feedbackin an industrial environment in accordance with the present disclosure.

FIG. 119 is a diagrammatic view that depicts an exemplary user interfacefor smart band configuration of a system for data collection in anindustrial environment is depicted in accordance with the presentdisclosure.

FIG. 120 is a diagrammatic view that depicts a wearable haptic userinterface device for providing haptic stimuli to a user that isresponsive to data collected in an industrial environment by a systemadapted to collect data in the industrial environment in accordance withthe present disclosure.

FIG. 121 is a diagrammatic view that depicts an augmented realitydisplay of heat maps based on data collected in an industrialenvironment by a system adapted to collect data in the environment inaccordance with the present disclosure.

FIG. 122 is a diagrammatic view that depicts an augmented realitydisplay including real time data overlaying a view of an industrialenvironment in accordance with the present disclosure.

FIG. 123 is a diagrammatic view that depicts a user interface displayand components of a neural net in a graphical user interface inaccordance with the present disclosure.

FIG. 124 is a diagrammatic view that depicts embodiments of a storagetime definition in accordance with the present disclosure.

FIG. 125 is a diagrammatic view that depicts embodiments of a dataresolution description in accordance with the present disclosure.

FIG. 126 is a diagrammatic view that depicts a smart heating system asan element in a network for in an industrial Internet of Thingsecosystem in accordance with the present disclosure.

FIG. 127 is a schematic of a data network including server and clientnodes coupled by intermediate networks.

FIG. 128 is a block diagram illustrating the modules that implementTCP-based communication between a client node and a server node.

FIG. 129 is a block diagram illustrating the modules that implementPacket Coding Transmission Communication Protocol (PC-TCP) basedcommunication between a client node and a server node.

FIG. 130 is a schematic diagram of a use of the PC-TCP basedcommunication between a server and a module device on a cellularnetwork.

FIG. 131 is a block diagram of 1 PC-TCP module that uses a conventionalUDP module.

FIG. 132 is a block diagram of a PC-TCP module that is partiallyintegrated into a client application and partially implemented using aconventional UDP module.

FIG. 133 is a block diagram or a PC-TCP module that is split with userspace and kernel space components.

FIG. 134 is a block diagram for a proxy architecture.

FIG. 135 is a block diagram of a PC-TCP based proxy architecture inwhich a proxy node communicates using both PC-TCP and conventional TCP.

FIG. 136 is a block diagram of a PC-TCP proxy-based architectureembodied using a gateway device.

FIG. 137 is a block diagram of an alternative proxy architectureembodied within a client node.

FIG. 138 is a block diagram of a second PC-TCP based proxy architecturein which a proxy node communicates using both PC-TCP and conventionalTCP.

FIG. 139 is a block diagram of a PC-TCP proxy-based architectureembodied using a wireless access device.

FIG. 140 is a block diagram of a PC-TCP proxy-based architectureembodied cellular network.

FIG. 141 is a block diagram of a PC-TCP proxy-based architectureembodied cable television-based data network.

FIG. 142 is a block diagram of an intermediate proxy that communicateswith a client node and with a server node using separate PC-TCPconnections.

FIG. 143 is a block diagram of a PC-TCP proxy-based architectureembodied in a network device.

FIG. 144 is a block diagram of an intermediate proxy that recodescommunication between a client node and with a server node.

FIGS. 145-146 are diagrams that illustrates delivery of common contentto multiple destinations.

FIGS. 147-157 are schematic diagrams of various embodiments of PC-TCPcommunication approaches.

FIG. 158 is a block diagram of PC-TCP communication approach thatincludes window and rate control modules.

FIG. 159 is a schematic of a data network.

FIGS. 160-163 are block diagrams illustrating an embodiment PC-TCPcommunication approach that is configured according to a number oftunable parameters.

FIG. 164 is a diagram showing a network communication system.

FIG. 165 is a schematic diagram illustrating use of stored communicationparameters.

FIG. 166 is a schematic diagram illustrating a first embodiment ormulti-path content delivery.

FIGS. 167-169 are schematic diagrams illustrating a second embodiment ofmulti-path content delivery.

FIG. 170 is a diagrammatic view depicting an integrated cooktop ofintelligent cooking system methods and systems in accordance with thepresent teachings.

FIG. 171 is a diagrammatic view depicting a single intelligent burner ofthe intelligent cooking system in accordance with the present teachings.

FIG. 172 is a partial exterior view depicting a solar-powered hydrogenproduction and storage station in accordance with the present teachings.

FIG. 173 is a diagrammatic view depicting a low-pressure storage systemin accordance with the present teachings.

FIG. 174 and FIG. 175 are cross-sectional views of a low-pressurestorage system.

FIG. 176 is a diagrammatic view depicting an electrolyzer in accordancewith the present teachings.

FIG. 177 is a diagrammatic view depicting features of a platform thatinteract with electronic devices and participants in a related ecosystemof suppliers, content providers, service providers, and regulators inaccordance with the present teachings.

FIG. 178 is a diagrammatic view depicting a smart home embodiment of theintelligent cooking system in accordance with the present teachings.

FIG. 179 is a diagrammatic view depicting a hydrogen production and usesystem in accordance with the present teachings.

FIG. 180 is a diagrammatic view depicting an electrolytic cell inaccordance with the present teachings.

FIG. 181 is a diagrammatic view depicting a hydrogen production systemintegrated into a cooking system in accordance with the presentteachings.

FIG. 182 is a diagrammatic view depicting auto switching connectivity inthe form of ad hoc Wi-Fi from the cooktop through nearby mobile devicesin a normal connectivity mode when Wi-Fi is available in accordance withthe present teachings.

FIG. 183 is a diagrammatic view depicting an auto switching connectivityin the form of ad hoc Wi Fi from the cooktop through nearby mobiledevices for ad hoc use of the local mobile devices for connectivity tothe cloud in accordance with the present teachings.

FIG. 184 is a perspective view depicting a three-element induction smartcooking system in accordance with the present teachings.

FIG. 185 is a perspective view depicting a single burner gas smartcooking system in accordance with the present teachings.

FIG. 186 is a perspective view depicting an electric hot plate smartcooking system in accordance with the present teachings.

FIG. 187 is a perspective view depicting a single induction heatingelement smart cooking system in accordance with the present teachings.

FIGS. 188-195 are views of visual interfaces depicting user interfacefeatures of a smart knob in accordance with the present teachings.

FIG. 196 is a perspective view depicting a smart knob deployed on asingle heating element cooking system in accordance with the presentteachings.

FIG. 197 is a partial perspective view depicting a smart knob deployedon a side of a kitchen appliance for a single heating element cookingsystem in accordance with the present teachings.

FIGS. 198-201 are perspective views depicting smart temperature probesof the smart cooking system in accordance with the present teachings.

FIGS. 202-207 are diagrammatic views depicting different docks forcompatibility with a range of smart phone and tablet devices inaccordance with the present teachings.

FIG. 208 and FIG. 209 are diagrammatic views depicting a burner designcontemplated for use with a smart cooking system in accordance with thepresent teachings.

FIG. 210 is a cross sectional view of a burner design contemplated foruse with a smart cooking system.

FIG. 211, FIG. 213, and FIG. 215 are diagrammatic views depicting aburner design contemplated for use with a smart cooking system, inaccordance with another example of the present teachings.

FIG. 212 and FIG. 214 are cross-sectional views of a burner design.

FIGS. 216-218 are diagrammatic views depicting a burner designcontemplated for use with a smart cooking system in accordance with afurther example of the present teachings.

FIGS. 219-221 are diagrammatic views depicting a burner designcontemplated for use with a smart cooking system in accordance with yetanother example of the present teachings.

FIG. 222 and FIG. 224 are diagrammatic views depicting a burner designcontemplated for use with a smart cooking system in accordance with anadditional example of the present teachings.

FIG. 223 is a cross-sectional view of a burner design contemplated foruse with a smart cooking system.

FIG. 225 is a flowchart depicting a method associated with a smartkitchen including a smart cooktop and an exhaust fan that may beautomatically turned on as water in a pot may begin to boil inaccordance with the present teachings.

FIG. 226 is an embodiment method and system related to renewable energysources for hydrogen production, storage, distribution and use aredepicted in accordance with the present teachings in accordance with thepresent teachings.

FIG. 227 is an alternate embodiment method and system related torenewable energy sources in accordance with the present teachings.

FIG. 228 is an alternate embodiment method and system related torenewable energy sources in accordance with the present teachings.

FIG. 229 depicts environments and manufacturing uses of hydrogenproduction, storage, distribution and use systems.

FIG. 230 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the present disclosure are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely exemplary of the disclosure, which may be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present disclosure in virtually anyappropriately detailed structure.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate with existing data collection, processing, and storage systemswhile preserving access to existing format/frequency range/resolutioncompatible data. While the industrial machine sensor data streamingfacilities described herein may collect a greater volume of data (e.g.,longer duration of data collection) from sensors at a wider range offrequencies and with greater resolution than existing data collectionsystems, methods and systems may be employed to provide access to datafrom the stream of data that represents one or more ranges of frequencyand/or one or more lines of resolution that are purposely compatiblewith existing systems. Further, a portion of the streamed data may beidentified, extracted, stored, and/or forwarded to existing dataprocessing systems to facilitate operation of existing data processingsystems that substantively matches operation of existing data processingsystems using existing collection-based data. In this way, a newlydeployed system for sensing aspects of industrial machines, such asaspects of moving parts of industrial machines, may facilitate continueduse of existing sensed data processing facilities, algorithms, models,pattern recognizers, user interfaces, and the like.

Through identification of existing frequency ranges, formats, and/orresolution, such as by accessing a data structure that defines theseaspects of existing data, higher resolution streamed data may beconfigured to represent a specific frequency, frequency range, format,and/or resolution. This configured streamed data can be stored in a datastructure that is compatible with existing sensed data structures sothat existing processing systems and facilities can access and processthe data substantially as if it were the existing data. One approach toadapting streamed data for compatibility with existing sensed data mayinclude aligning the streamed data with existing data so that portionsof the streamed data that align with the existing data can be extracted,stored, and made available for processing with existing data processingmethods. Alternatively, data processing methods may be configured toprocess portions of the streamed data that correspond, such as throughalignment, to the existing data, with methods that implement functionssubstantially similar to the methods used to process existing data, suchas methods that process data that contain a particular frequency rangeor a particular resolution and the like.

Methods used to process existing data may be associated with certaincharacteristics of sensed data, such as certain frequency ranges,sources of data, and the like. As an example, methods for processingbearing sensing information for a moving part of an industrial machinemay be capable of processing data from bearing sensors that fall into aparticular frequency range. This method can thusly be at least partiallyidentifiable by these characteristics of the data being processed.Therefore, given a set of conditions, such as moving device beingsensed, industrial machine type, frequency of data being sensed, and thelike, a data processing system may select an appropriate method. Also,given such a set of conditions, an industrial machine data sensing andprocessing facility may configure elements, such as data filters,routers, processors, and the like, to handle data meeting theconditions.

FIGS. 1 through 5 depict portions of an overall view of an industrialInternet of Things (IoT) data collection, monitoring and control system10. FIG. 2 depicts a mobile ad hoc network (“MANET”) 20, which may forma secure, temporal network connection 22 (sometimes connected andsometimes isolated), with a cloud 30 or other remote networking system,so that network functions may occur over the MANET 20 within theenvironment, without the need for external networks, but at other timesinformation can be sent to and from a central location. This allows theindustrial environment to use the benefits of networking and controltechnologies, while also providing security, such as preventingcyber-attacks. The MANET 20 may use cognitive radio technologies 40,including those that form up an equivalent to the IP protocol, such asrouter 42, MAC 44, and physical layer technologies 46. In certainembodiments, the system depicted in FIGS. 1 through 5 providesnetwork-sensitive or network-aware transport of data over the network toand from a data collection device or a heavy industrial machine.

FIGS. 3-4 depict intelligent data collection technologies deployedlocally, at the edge of an IoT deployment, where heavy industrialmachines are located. This includes various sensors 52, IoT devices 54,data storage capabilities (e.g., data pools 60, or distributed ledger62) (including intelligent, self-organizing storage), sensor fusion(including self-organizing sensor fusion), and the like. Interfaces fordata collection, including multi-sensory interfaces, tablets,smartphones 58, and the like are shown. FIG. 3 also shows data pools 60that may collect data published by machines or sensors that detectconditions of machines, such as for later consumption by local or remoteintelligence. A distributed ledger system 62 may distribute storageacross the local storage of various elements of the environment, or morebroadly throughout the system. FIG. 4 also shows on-device sensor fusion80, such as for storing on a device data from multiple analog sensors82, which may be analyzed locally or in the cloud, such as by machinelearning 84, including by training a machine based on initial modelscreated by humans that are augmented by providing feedback (such asbased on measures of success) when operating the methods and systemsdisclosed herein.

FIG. 1 depicts a server based portion of an industrial IoT system thatmay be deployed in the cloud or on an enterprise owner's or operator'spremises. The server portion includes network coding (includingself-organizing network coding and/or automated configuration) that mayconfigure a network coding model based on feedback measures, networkconditions, or the like, for highly efficient transport of large amountsof data across the network to and from data collection systems and thecloud. Network coding may provide a wide range of capabilities forintelligence, analytics, remote control, remote operation, remoteoptimization, various storage configurations and the like, as depictedin FIG. 1. The various storage configurations may include distributedledger storage for supporting transactional data or other elements ofthe system.

FIG. 5 depicts a programmatic data marketplace 70, which may be aself-organizing marketplace, such as for making available data that iscollected in industrial environments, such as from data collectors, datapools, distributed ledgers, and other elements disclosed herein.Additional detail on the various components and sub-components of FIGS.1 through 5 is provided throughout this disclosure.

With reference to FIG. 6, an embodiment of platform 100 may include alocal data collection system 102, which may be disposed in anenvironment 104, such as an industrial environment similar to that shownin FIG. 3, for collecting data from or about the elements of theenvironment, such as machines, components, systems, sub-systems, ambientconditions, states, workflows, processes, and other elements. Theplatform 100 may connect to or include portions of the industrial IoTdata collection, monitoring and control system 10 depicted in FIGS. 1-5.The platform 100 may include a network data transport system 108, suchas for transporting data to and from the local data collection system102 over a network 110, such as to a host processing system 112, such asone that is disposed in a cloud computing environment or on the premisesof an enterprise, or that consists of distributed components thatinteract with each other to process data collected by the local datacollection system 102. The host processing system 112, referred to forconvenience in some cases as the host system 112, may include varioussystems, components, methods, processes, facilities, and the like forenabling automated, or automation-assisted processing of the data, suchas for monitoring one or more environments 104 or networks 110 or forremotely controlling one or more elements in a local environment 104 orin a network 110. The platform 100 may include one or more localautonomous systems, such as for enabling autonomous behavior, such asreflecting artificial, or machine-based intelligence or such as enablingautomated action based on the applications of a set of rules or modelsupon input data from the local data collection system 102 or from one ormore input sources 116, which may comprise information feeds and inputsfrom a wide array of sources, including those in the local environment104, in a network 110, in the host system 112, or in one or moreexternal systems, databases, or the like. The platform 100 may includeone or more intelligent systems 118, which may be disposed in,integrated with, or acting as inputs to one or more components of theplatform 100. Details of these and other components of the platform 100are provided throughout this disclosure.

Intelligent systems 118 may include cognitive systems 120, such asenabling a degree of cognitive behavior as a result of the coordinationof processing elements, such as mesh, peer-to-peer, ring, serial, andother architectures, where one or more node elements is coordinated withother node elements to provide collective, coordinated behavior toassist in processing, communication, data collection, or the like. TheMANET 20 depicted in FIG. 2 may also use cognitive radio technologies,including those that form up an equivalent to the IP protocol, such asrouter 42, MAC 44, and physical layer technologies 46. In one example,the cognitive system technology stack can include examples disclosed inU.S. Pat. No. 8,060,017 to Schlicht et al., issued 15 Nov. 2011 andhereby incorporated by reference as if fully set forth herein.

Intelligent systems may include machine learning systems 122, such asfor learning on one or more data sets. The one or more data sets mayinclude information collected using local data collection systems 102 orother information from input sources 116, such as to recognize states,objects, events, patterns, conditions, or the like that may, in turn, beused for processing by the host system 112 as inputs to components ofthe platform 100 and portions of the industrial IoT data collection,monitoring and control system 10, or the like. Learning may behuman-supervised or fully-automated, such as using one or more inputsources 116 to provide a data set, along with information about the itemto be learned. Machine learning may use one or more models, rules,semantic understandings, workflows, or other structured orsemi-structured understanding of the world, such as for automatedoptimization of control of a system or process based on feedback or feedforward to an operating model for the system or process. One suchmachine learning technique for semantic and contextual understandings,workflows, or other structured or semi-structured understandings isdisclosed in U.S. Pat. No. 8,200,775 to Moore, issued 12 Jun. 2012, andhereby incorporated by reference as if fully set forth herein. Machinelearning may be used to improve the foregoing, such as by adjusting oneor more weights, structures, rules, or the like (such as changing afunction within a model) based on feedback (such as regarding thesuccess of a model in a given situation) or based on iteration (such asin a recursive process). Where sufficient understanding of theunderlying structure or behavior of a system is not known, insufficientdata is not available, or in other cases where preferred for variousreasons, machine learning may also be undertaken in the absence of anunderlying model; that is, input sources may be weighted, structured, orthe like within a machine learning facility without regard to any apriori understanding of structure, and outcomes (such as those based onmeasures of success at accomplishing various desired objectives) can beserially fed to the machine learning system to allow it to learn how toachieve the targeted objectives. For example, the system may learn torecognize faults, to recognize patterns, to develop models or functions,to develop rules, to optimize performance, to minimize failure rates, tooptimize profits, to optimize resource utilization, to optimize flow(such as flow of traffic), or to optimize many other parameters that maybe relevant to successful outcomes (such as outcomes in a wide range ofenvironments). Machine learning may use genetic programming techniques,such as promoting or demoting one or more input sources, structures,data types, objects, weights, nodes, links, or other factors based onfeedback (such that successful elements emerge over a series ofgenerations). For example, alternative available sensor inputs for adata collection system 102 may be arranged in alternative configurationsand permutations, such that the system may, using generic programmingtechniques over a series of data collection events, determine whatpermutations provide successful outcomes based on various conditions(such as conditions of components of the platform 100, conditions of thenetwork 110, conditions of a data collection system 102, conditions ofan environment 104), or the like. In embodiments, local machine learningmay turn on or off one or more sensors in a multi-sensor data collector102 in permutations over time, while tracking success outcomes such ascontributing to success in predicting a failure, contributing to aperformance indicator (such as efficiency, effectiveness, return oninvestment, yield, or the like), contributing to optimization of one ormore parameters, identification of a pattern (such as relating to athreat, a failure mode, a success mode, or the like) or the like. Forexample, a system may learn what sets of sensors should be turned on oroff under given conditions to achieve the highest value utilization of adata collector 102. In embodiments, similar techniques may be used tohandle optimization of transport of data in the platform 100 (such as inthe network 110) by using generic programming or other machine learningtechniques to learn to configure network elements (such as configuringnetwork transport paths, configuring network coding types andarchitectures, configuring network security elements), and the like.

In embodiments, the local data collection system 102 may include ahigh-performance, multi-sensor data collector having a number of novelfeatures for collection and processing of analog and other sensor data.In embodiments, a local data collection system 102 may be deployed tothe industrial facilities depicted in FIG. 3. A local data collectionsystem 102 may also be deployed monitor other machines such as themachine 2300 in FIG. 9 and FIG. 10, the machines 2400, 2600, 2800, 2950,3000 depicted in FIG. 12, and the machines 3202, 3204 depicted in FIG.13. The data collection system 102 may have on-board intelligent systems118 (such as for learning to optimize the configuration and operation ofthe data collector, such as configuring permutations and combinations ofsensors based on contexts and conditions). In one example, the datacollection system 102 includes a crosspoint switch 130 or other analogswitch. Automated, intelligent configuration of the local datacollection system 102 may be based on a variety of types of information,such as information from various input sources, including those based onavailable power, power requirements of sensors, the value of the datacollected (such as based on feedback information from other elements ofthe platform 100), the relative value of information (such as valuesbased on the availability of other sources of the same or similarinformation), power availability (such as for powering sensors), networkconditions, ambient conditions, operating states, operating contexts,operating events, and many others.

FIG. 7 shows elements and sub-components of a data collection andanalysis system 1100 for sensor data (such as analog sensor data)collected in industrial environments. As depicted in FIG. 7, embodimentsof the methods and systems disclosed herein may include hardware thathas several different modules starting with the multiplexer (“MUX”) mainboard 1104. In embodiments, there may be a MUX option board 1108. TheMUX 114 main board is where the sensors connect to the system. Theseconnections are on top to enable ease of installation. Then there arenumerous settings on the underside of this board as well as on the Muxoption board 1108, which attaches to the MUX main board 1104 via twoheaders one at either end of the board. In embodiments, the Mux optionboard has the male headers, which mesh together with the female headeron the main Mux board. This enables them to be stacked on top of eachother taking up less real estate.

In embodiments, the main Mux board and/or the MUX option board thenconnects to the mother (e.g., with 4 simultaneous channels) and daughter(e.g., with 4 additional channels for 8 total channels) analog boards1110 via cables where some of the signal conditioning (such as hardwareintegration) occurs. The signals then move from the analog boards 1110to an anti-aliasing board (not shown) where some of the potentialaliasing is removed. The rest of the aliasing removal is done on thedelta sigma board 1112. The delta sigma board 1112 provides morealiasing protection along with other conditioning and digitizing of thesignal. Next, the data moves to the Jennic™ board 1114 for moredigitizing as well as communication to a computer via USB or Ethernet.In embodiments, the Jennic™ board 1114 may be replaced with a pic board1118 for more advanced and efficient data collection as well ascommunication. Once the data moves to the computer software 1102, thecomputer software 1102 can manipulate the data to show trending,spectra, waveform, statistics, and analytics.

In embodiments, the system is meant to take in all types of data fromvolts to 4-20 mA signals. In embodiments, open formats of data storageand communication may be used. In some instances, certain portions ofthe system may be proprietary especially some of research and dataassociated with the analytics and reporting. In embodiments, smart bandanalysis is a way to break data down into easily analyzed parts that canbe combined with other smart bands to make new more simplified yetsophisticated analytics. In embodiments, this unique information istaken and graphics are used to depict the conditions because picturedepictions are more helpful to the user. In embodiments, complicatedprograms and user interfaces are simplified so that any user canmanipulate the data like an expert.

In embodiments, the system in essence, works in a big loop. The systemstarts in software with a general user interface (“GUI”) 1124. Inembodiments, rapid route creation may take advantage of hierarchicaltemplates. In embodiments, a GUI is created so any general user canpopulate the information itself with simple templates. Once thetemplates are created the user can copy and paste whatever the userneeds. In addition, users can develop their own templates for futureease of use and to institutionalize the knowledge. When the user hasentered all of the user's information and connected all of the user'ssensors, the user can then start the system acquiring data.

Embodiments of the methods and systems disclosed herein may includeunique electrostatic protection for trigger and vibration inputs. Inmany critical industrial environments where large electrostatic forces,which can harm electrical equipment, may build up, for example rotatingmachinery or low-speed balancing using large belts, proper transducerand trigger input protection is required. In embodiments, a low-cost butefficient method is described for such protection without the need forexternal supplemental devices.

Typically, vibration data collectors are not designed to handle largeinput voltages due to the expense and the fact that, more often thannot, it is not needed. A need exists for these data collectors toacquire many varied types of RPM data as technology improves andmonitoring costs plummet. In embodiments, a method is using the alreadyestablished OptoMOS™ technology which permits the switching up front ofhigh voltage signals rather than using more conventional reed-relayapproaches. Many historic concerns regarding non-linear zero crossing orother non-linear solid-state behaviors have been eliminated with regardto the passing through of weakly buffered analog signals. In addition,in embodiments, printed circuit board routing topologies place all ofthe individual channel input circuitry as close to the input connectoras possible. In embodiments, a unique electrostatic protection fortrigger and vibration inputs may be placed upfront on the Mux and DAQhardware in order to dissipate the built up electric charge as thesignal passed from the sensor to the hardware. In embodiments, the Muxand analog board may support high-amperage input using a design topologycomprising wider traces and solid state relays for upfront circuitry.

In some systems multiplexers are afterthoughts and the quality of thesignal coming from the multiplexer is not considered. As a result of apoor quality multiplexer, the quality of the signal can drop as much as30 dB or more. Thus, substantial signal quality may be lost using a24-bit DAQ that has a signal to noise ratio of 110 dB and if the signalto noise ratio drops to 80 dB in the Mux, it may not be much better thana 16-bit system from 20 years ago. In embodiments of this system, animportant part at the front of the Mux is upfront signal conditioning onMux for improved signal-to-noise ratio. Embodiments may perform signalconditioning (such as range/gain control, integration, filtering, etc.)on vibration as well as other signal inputs up front before Muxswitching to achieve the highest signal-to-noise ratio.

In embodiments, in addition to providing a better signal, themultiplexer may provide a continuous monitor alarming feature. Trulycontinuous systems monitor every sensor all the time but tend to beexpensive. Typical multiplexer systems only monitor a set number ofchannels at one time and switch from bank to bank of a larger set ofsensors. As a result, the sensors not being currently collected are notbeing monitored; if a level increases the user may never know. Inembodiments, a multiplexer may have a continuous monitor alarmingfeature by placing circuitry on the multiplexer that can measure inputchannel levels against known alarm conditions even when the dataacquisition (“DAQ”) is not monitoring the input. In embodiments,continuous monitoring Mux bypass offers a mechanism whereby channels notbeing currently sampled by the Mux system may be continuously monitoredfor significant alarm conditions via a number of trigger conditionsusing filtered peak-hold circuits or functionally similar that are inturn passed on to the monitoring system in an expedient manner usinghardware interrupts or other means. This, in essence, makes the systemcontinuously monitoring, although without the ability to instantlycapture data on the problem like a true continuous system. Inembodiments, coupling this capability to alarm with adaptive schedulingtechniques for continuous monitoring and the continuous monitoringsystem's software adapting and adjusting the data collection sequencebased on statistics, analytics, data alarms and dynamic analysis mayallow the system to quickly collect dynamic spectral data on thealarming sensor very soon after the alarm sounds.

Another restriction of typical multiplexers is that they may have alimited number of channels. In embodiments, use of distributed complexprogrammable logic device (“CPLD”) chips with dedicated bus for logiccontrol of multiple Mux and data acquisition sections enables a CPLD tocontrol multiple mux and DAQs so that there is no limit to the number ofchannels a system can handle Interfacing to multiple types of predictivemaintenance and vibration transducers requires a great deal ofswitching. This includes AC/DC coupling, 4-20 interfacing, integratedelectronic piezoelectric transducer, channel power-down (for conservingop-amp power), single-ended or differential grounding options, and soon. Also required is the control of digital pots for range and gaincontrol, switches for hardware integration, AA filtering and triggering.This logic can be performed by a series of CPLD chips strategicallylocated for the tasks they control. A single giant CPLD requires longcircuit routes with a great deal of density at the single giant CPLD. Inembodiments, distributed CPLDs not only address these concerns but offera great deal of flexibility. A bus is created where each CPLD that has afixed assignment has its own unique device address. In embodiments,multiplexers and DAQs can stack together offering additional input andoutput channels to the system. For multiple boards (e.g., for multipleMux boards), jumpers are provided for setting multiple addresses. Inanother example, three bits permit up to 8 boards that are jumperconfigurable. In embodiments, a bus protocol is defined such that eachCPLD on the bus can either be addressed individually or as a group.

Typical multiplexers may be limited to collecting only sensors in thesame bank. For detailed analysis, this may be limiting as there istremendous value in being able to simultaneously review data fromsensors on the same machine. Current systems using conventional fixedbank multiplexers can only compare a limited number of channels (basedon the number of channels per bank) that were assigned to a particulargroup at the time of installation. The only way to provide someflexibility is to either overlap channels or incorporate lots ofredundancy in the system both of which can add considerable expense (insome cases an exponential increase in cost versus flexibility). Thesimplest Mux design selects one of many inputs and routes it into asingle output line. A banked design would consist of a group of thesesimple building blocks, each handling a fixed group of inputs androuting to its respective output. Typically, the inputs are notoverlapping so that the input of one Mux grouping cannot be routed intoanother. Unlike conventional Mux chips which typically switch a fixedgroup or banks of a fixed selection of channels into a single output(e.g., in groups of 2, 4, 8, etc.), a cross point Mux allows the user toassign any input to any output. Previously, crosspoint multiplexers wereused for specialized purposes such as RGB digital video applications andwere as a practical matter too noisy for analog applications such asvibration analysis; however more recent advances in the technology nowmake it feasible. Another advantage of the crosspoint Mux is the abilityto disable outputs by putting them into a high impedance state. This isideal for an output bus so that multiple Mux cards may be stacked, andtheir output buses joined together without the need for bus switches.

In embodiments, this may be addressed by use of an analog crosspointswitch for collecting variable groups of vibration input channels andproviding a matrix circuit so the system may access any set of eightchannels from the total number of input sensors.

In embodiments, the ability to control multiple multiplexers with use ofdistributed CPLD chips with dedicated bus for logic control of multipleMux and data acquisition sections is enhanced with a hierarchicalmultiplexer which allows for multiple DAQ to collect data from multiplemultiplexers. A hierarchical Mux may allow modularly output of morechannels, such as 16, 24 or more to multiple of eight channel card sets.In embodiments, this allows for faster data collection as well as morechannels of simultaneous data collection for more complex analysis. Inembodiments, the Mux may be configured slightly to make it portable anduse data acquisition parking features, which turns SV3X DAQ into aprotected system embodiment.

In embodiments, once the signals leave the multiplexer and hierarchicalMux they move to the analog board where there are other enhancements. Inembodiments, power saving techniques may be used such as: power-down ofanalog channels when not in use; powering down of component boards;power-down of analog signal processing op-amps for non-selectedchannels; powering down channels on the mother and the daughter analogboards. The ability to power down component boards and other hardware bythe low-level firmware for the DAQ system makes high-level applicationcontrol with respect to power-saving capabilities relatively easy.Explicit control of the hardware is always possible but not required bydefault. In embodiments, this power saving benefit may be of value to aprotected system, especially if it is battery operated or solar powered.

In embodiments, in order to maximize the signal to noise ratio andprovide the best data, a peak-detector for auto-scaling routed into aseparate A/D will provide the system the highest peak in each set ofdata so it can rapidly scale the data to that peak. For vibrationanalysis purposes, the built-in A/D convertors in many microprocessorsmay be inadequate with regards to number of bits, number of channels orsampling frequency versus not slowing the microprocessor downsignificantly. Despite these limitations, it is useful to use them forpurposes of auto-scaling. In embodiments, a separate A/D may be usedthat has reduced functionality and is cheaper. For each channel ofinput, after the signal is buffered (usually with the appropriatecoupling: AC or DC) but before it is signal conditioned, the signal isfed directly into the microprocessor or low-cost A/D. Unlike theconditioned signal for which range, gain and filter switches are thrown,no switches are varied. This permits the simultaneous sampling of theauto-scaling data while the input data is signal conditioned, fed into amore robust external A/D, and directed into on-board memory using directmemory access (DMA) methods where memory is accessed without requiring aCPU. This significantly simplifies the auto-scaling process by nothaving to throw switches and then allow for settling time, which greatlyslows down the auto-scaling process. Furthermore, the data may becollected simultaneously, which assures the best signal-to-noise ratio.The reduced number of bits and other features is usually more thanadequate for auto-scaling purposes. In embodiments, improved integrationusing both analog and digital methods create an innovative hybridintegration which also improves or maintains the highest possible signalto noise ratio.

In embodiments, a section of the analog board may allow routing of atrigger channel, either raw or buffered, into other analog channels.This may allow a user to route the trigger to any of the channels foranalysis and trouble shooting. Systems may have trigger channels for thepurposes of determining relative phase between various input data setsor for acquiring significant data without the needless repetition ofunwanted input. In embodiments, digitally controlled relays may be usedto switch either the raw or buffered trigger signal into one of theinput channels. It may be desirable to examine the quality of thetriggering pulse because it may be corrupted for a variety of reasonsincluding inadequate placement of the trigger sensor, wiring issues,faulty setup issues such as a dirty piece of reflective tape if using anoptical sensor, and so on. The ability to look at either the raw orbuffered signal may offer an excellent diagnostic or debugging vehicle.It also can offer some improved phase analysis capability by making useof the recorded data signal for various signal processing techniquessuch as variable speed filtering algorithms.

In embodiments, once the signals leave the analog board, the signalsmove into the delta-sigma board where precise voltage reference for A/Dzero reference offers more accurate direct current sensor data. Thedelta sigma's high speeds also provide for using higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize antialiasing filter requirements. Lower oversampling rates canbe used for higher sampling rates. For example, a 3^(rd) order AA filterset for the lowest sampling requirement for 256 Hz (Fmax of 100 Hz) isthen adequate for Fmax ranges of 200 and 500 Hz. Another higher-cutoffAA filter can then be used for Fmax ranges from 1 kHz and higher (with asecondary filter kicking in at 2.56× the highest sampling rate of 128kHz). In embodiments, a CPLD may be used as a clock-divider for adelta-sigma A/D to achieve lower sampling rates without the need fordigital resampling. In embodiments, a high-frequency crystal referencecan be divided down to lower frequencies by employing a CPLD as aprogrammable clock divider. The accuracy of the divided down lowerfrequencies is even more accurate than the original source relative totheir longer time periods. This also minimizes or removes the need forresampling processing by the delta-sigma A/D.

In embodiments, the data then moves from the delta-sigma board to theJennic™ board where phase relative to input and trigger channels usingon-board timers may be digitally derived. In embodiments, the Jennic™board also has the ability to store calibration data and systemmaintenance repair history data in an on-board card set. In embodiments,the Jennic™ board will enable acquiring long blocks of data athigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates so it can stream data and acquire long blocksof data for advanced analysis in the future.

In embodiments, after the signal moves through the Jennic™ board it maythen be transmitted to the computer. In embodiments, the computersoftware will be used to add intelligence to the system starting with anexpert system GUI. The GUI will offer a graphical expert system withsimplified user interface for defining smart bands and diagnoses whichfacilitate anyone to develop complex analytics. In embodiments, thisuser interface may revolve around smart bands, which are a simplifiedapproach to complex yet flexible analytics for the general user. Inembodiments, the smart bands may pair with a self-learning neuralnetwork for an even more advanced analytical approach. In embodiments,this system may use the machine's hierarchy for additional analyticalinsight. One critical part of predictive maintenance is the ability tolearn from known information during repairs or inspections. Inembodiments, graphical approaches for back calculations may improve thesmart bands and correlations based on a known fault or problem.

In embodiments, there is a smart route which adapts which sensors itcollects simultaneously in order to gain additional correlativeintelligence. In embodiments, smart operational data store (“ODS”)allows the system to elect to gather data to perform operationaldeflection shape analysis in order to further examine the machinerycondition. In embodiments, adaptive scheduling techniques allow thesystem to change the scheduled data collected for full spectral analysisacross a number (e.g., eight), of correlative channels. In embodiments,the system may provide data to enable extended statistics capabilitiesfor continuous monitoring as well as ambient local vibration foranalysis that combines ambient temperature and local temperature andvibration levels changes for identifying machinery issues.

In embodiments, a data acquisition device may be controlled by apersonal computer (PC) to implement the desired data acquisitioncommands. In embodiments, the DAQ box may be self-sufficient, and canacquire, process, analyze and monitor independent of external PCcontrol. Embodiments may include secure digital (SD) card storage. Inembodiments, significant additional storage capability may be providedby utilizing an SD card. This may prove critical for monitoringapplications where critical data may be stored permanently. Also, if apower failure should occur, the most recent data may be stored despitethe fact that it was not off-loaded to another system.

A current trend has been to make DAQ systems as communicative aspossible with the outside world usually in the form of networksincluding wireless. In the past it was common to use a dedicated bus tocontrol a DAQ system with either a microprocessor ormicrocontroller/microprocessor paired with a PC. In embodiments, a DAQsystem may comprise one or more microprocessor/microcontrollers,specialized microcontrollers/microprocessors, or dedicated processorsfocused primarily on the communication aspects with the outside world.These include USB, Ethernet and wireless with the ability to provide anIP address or addresses in order to host a webpage. All communicationswith the outside world are then accomplished using a simple text basedmenu. The usual array of commands (in practice more than a hundred) suchas InitializeCard, AcquireData, StopAcquisition, RetrieveCalibrationInfo, and so on, would be provided.

In embodiments, intense signal processing activities includingresampling, weighting, filtering, and spectrum processing may beperformed by dedicated processors such as field-programmable gate array(“FPGAs”), digital signal processor (“DSP”), microprocessors,micro-controllers, or a combination thereof. In embodiments, thissubsystem may communicate via a specialized hardware bus with thecommunication processing section. It will be facilitated with dual-portmemory, semaphore logic, and so on. This embodiment will not onlyprovide a marked improvement in efficiency but can significantly improvethe processing capability, including the streaming of the data as wellother high-end analytical techniques. This negates the need forconstantly interrupting the main processes which include the control ofthe signal conditioning circuits, triggering, raw data acquisition usingthe A/D, directing the A/D output to the appropriate on-board memory andprocessing that data.

Embodiments may include sensor overload identification. A need existsfor monitoring systems to identify when the sensor is overloading. Theremay be situations involving high-frequency inputs that will saturate astandard 100 mv/g sensor (which is most commonly used in the industry)and having the ability to sense the overload improves data quality forbetter analysis. A monitoring system may identify when their system isoverloading, but in embodiments, the system may look at the voltage ofthe sensor to determine if the overload is from the sensor, enabling theuser to get another sensor better suited to the situation, or gather thedata again.

Embodiments may include radio frequency identification (“RFID”) and aninclinometer or accelerometer on a sensor so the sensor can indicatewhat machine/bearing it is attached to and what direction such that thesoftware can automatically store the data without the user input. Inembodiments, users could put the system on any machine or machines andthe system would automatically set itself up and be ready for datacollection in seconds.

Embodiments may include ultrasonic online monitoring by placingultrasonic sensors inside transformers, motor control centers, breakersand the like and monitoring, via a sound spectrum, continuously lookingfor patterns that identify arcing, corona and other electrical issuesindicating a break down or issue. Embodiments may include providingcontinuous ultrasonic monitoring of rotating elements and bearings of anenergy production facility. In embodiments, an analysis engine may beused in ultrasonic online monitoring as well as identifying other faultsby combining the ultrasonic data with other parameters such asvibration, temperature, pressure, heat flux, magnetic fields, electricalfields, currents, voltage, capacitance, inductance, and combinations(e.g., simple ratios) of the same, among many others.

Embodiments of the methods and systems disclosed herein may include useof an analog crosspoint switch for collecting variable groups ofvibration input channels. For vibration analysis, it is useful to obtainmultiple channels simultaneously from vibration transducers mounted ondifferent parts of a machine (or machines) in multiple directions. Byobtaining the readings at the same time, for example, the relativephases of the inputs may be compared for the purpose of diagnosingvarious mechanical faults. Other types of cross channel analyses such ascross-correlation, transfer functions, Operating Deflection Shape(“ODS”) may also be performed.

Embodiments of the methods and systems disclosed herein may includeprecise voltage reference for A/D zero reference. Some A/D chips providetheir own internal zero voltage reference to be used as a mid-scalevalue for external signal conditioning circuitry to ensure that both theA/D and external op-amps use the same reference. Although this soundsreasonable in principle, there are practical complications. In manycases these references are inherently based on a supply voltage using aresistor-divider. For many current systems, especially those whose poweris derived from a PC via USB or similar bus, this provides for anunreliable reference, as the supply voltage will often vary quitesignificantly with load. This is especially true for delta-sigma A/Dchips which necessitate increased signal processing. Although theoffsets may drift together with load, a problem arises if one wants tocalibrate the readings digitally. It is typical to modify the voltageoffset expressed as counts coming from the A/D digitally to compensatefor the DC drift. However, for this case, if the proper calibrationoffset is determined for one set of loading conditions, they will notapply for other conditions. An absolute DC offset expressed in countswill no longer be applicable. As a result, it becomes necessary tocalibrate for all loading conditions which becomes complex, unreliable,and ultimately unmanageable. In embodiments, an external voltagereference is used which is simply independent of the supply voltage touse as the zero offset.

In embodiments, the system provides a phase-lock-loop band pass trackingfilter method for obtaining slow-speed RPMs and phase for balancingpurposes to remotely balance slow speed machinery, such as in papermills, as well as offering additional analysis from its data. Forbalancing purposes, it is sometimes necessary to balance at very slowspeeds. A typical tracking filter may be constructed based on aphase-lock loop or PLL design; however, stability and speed range areoverriding concerns. In embodiments, a number of digitally controlledswitches are used for selecting the appropriate RC and dampingconstants. The switching can be done all automatically after measuringthe frequency of the incoming tach signal. Embodiments of the methodsand systems disclosed herein may include digital derivation of phaserelative to input and trigger channels using on-board timers. Inembodiments, digital phase derivation uses digital timers to ascertainan exact delay from a trigger event to the precise start of dataacquisition. This delay, or offset, then, is further refined usinginterpolation methods to obtain an even more precise offset which isthen applied to the analytically determined phase of the acquired datasuch that the phase is “in essence” an absolute phase with precisemechanical meaning useful for among other things, one-shot balancing,alignment analysis, and so on.

Embodiments of the methods and systems disclosed herein may includesignal processing firmware/hardware. In embodiments, long blocks of datamay be acquired at high-sampling rate as opposed to multiple sets ofdata taken at different sampling rates. Typically, in modern routecollection for vibration analysis, it is customary to collect data at afixed sampling rate with a specified data length. The sampling rate anddata length may vary from route point to point based on the specificmechanical analysis requirements at hand. For example, a motor mayrequire a relatively low sampling rate with high resolution todistinguish running speed harmonics from line frequency harmonics. Thepractical trade-off here though is that it takes more collection time toachieve this improved resolution. In contrast, some high-speedcompressors or gear sets require much higher sampling rates to measurethe amplitudes of relatively higher frequency data although the preciseresolution may not be as necessary. Ideally, however, it would be betterto collect a very long sample length of data at a very high-samplingrate. When digital acquisition devices were first popularized in theearly 1980's, the A/D sampling, digital storage, and computationalabilities were not close to what they are today, so compromises weremade between the time required for data collection and the desiredresolution and accuracy. It was because of this limitation that someanalysts in the field even refused to give up their analog taperecording systems, which did not suffer as much from these samedigitizing drawbacks. A few hybrid systems were employed that woulddigitize the play back of the recorded analog data at multiple samplingrates and lengths desired, though these systems were admittedly lessautomated. The more common approach, as mentioned earlier, is to balancedata collection time with analysis capability and digitally acquire thedata blocks at multiple sampling rates and sampling lengths anddigitally store these blocks separately. In embodiments, a long datalength of data can be collected at the highest practical sampling rate(e.g., 102.4 kHz; corresponding to a 40 kHz Fmax) and stored. This longblock of data can be acquired in the same amount of time as the shorterlength of the lower sampling rates utilized by a priori methods so thatthere is no effective delay added to the sampling at the measurementpoint, always a concern in route collection. In embodiments, analog taperecording of data is digitally simulated with such a precision that itcan be in effect considered continuous or “analog” for many purposes,including for purposes of embodiments of the present disclosure, exceptwhere context indicates otherwise.

Embodiments of the methods and systems disclosed herein may includestorage of calibration data and maintenance history on-board card sets.Many data acquisition devices which rely on interfacing to a PC tofunction store their calibration coefficients on the PC. This isespecially true for complex data acquisition devices whose signal pathsare many and therefore whose calibration tables can be quite large. Inembodiments, calibration coefficients are stored in flash memory whichwill remember this data or any other significant information for thatmatter, for all practical purposes, permanently. This information mayinclude nameplate information such as serial numbers of individualcomponents, firmware or software version numbers, maintenance history,and the calibration tables. In embodiments, no matter which computer thebox is ultimately connected to, the DAQ box remains calibrated andcontinues to hold all of this critical information. The PC or externaldevice may poll for this information at any time for implantation orinformation exchange purposes.

Embodiments of the methods and systems disclosed herein may includerapid route creation taking advantage of hierarchical templates. In thefield of vibration monitoring, as well as parametric monitoring ingeneral, it is necessary to establish in a database or functionalequivalent the existence of data monitoring points. These points areassociated a variety of attributes including the following categories:transducer attributes, data collection settings, machinery parametersand operating parameters. The transducer attributes would include probetype, probe mounting type and probe mounting direction or axisorientation. Data collection attributes associated with the measurementwould involve a sampling rate, data length, integrated electronicpiezoelectric probe power and coupling requirements, hardwareintegration requirements, 4-20 or voltage interfacing, range and gainsettings (if applicable), filter requirements, and so on. Machineryparametric requirements relative to the specific point would includesuch items as operating speed, bearing type, bearing parametric datawhich for a rolling element bearing includes the pitch diameter, numberof balls, inner race, and outer-race diameters. For a tilting padbearing, this would include the number of pads and so on. Formeasurement points on a piece of equipment such as a gearbox, neededparameters would include, for example, the number of gear teeth on eachof the gears. For induction motors, it would include the number of rotorbars and poles; for compressors, the number of blades and/or vanes; forfans, the number of blades. For belt/pulley systems, the number of beltsas well as the relevant belt-passing frequencies may be calculated fromthe dimensions of the pulleys and pulley center-to-center distance. Formeasurements near couplings, the coupling type and number of teeth in ageared coupling may be necessary, and so on. Operating parametric datawould include operating load, which may be expressed in megawatts, flow(either air or fluid), percentage, horsepower, feet-per-minute, and soon. Operating temperatures both ambient and operational, pressures,humidity, and so on, may also be relevant. As can be seen, the setupinformation required for an individual measurement point can be quitelarge. It is also crucial to performing any legitimate analysis of thedata. Machinery, equipment, and bearing specific information areessential for identifying fault frequencies as well as anticipating thevarious kinds of specific faults to be expected. The transducerattributes as well as data collection parameters are vital for properlyinterpreting the data along with providing limits for the type ofanalytical techniques suitable. The traditional means of entering thisdata has been manual and quite tedious, usually at the lowesthierarchical level (for example, at the bearing level with regards tomachinery parameters), and at the transducer level for data collectionsetup information. It cannot be stressed enough, however, the importanceof the hierarchical relationships necessary to organize data—both foranalytical and interpretive purposes as well as the storage and movementof data. Here, we are focusing primarily on the storage and movement ofdata. By its nature, the aforementioned setup information is extremelyredundant at the level of the lowest hierarchies; however, because ofits strong hierarchical nature, it can be stored quite efficiently inthat form. In embodiments, hierarchical nature can be utilized whencopying data in the form of templates. As an example, hierarchicalstorage structure suitable for many purposes is defined from general tospecific of company, plant or site, unit or process, machine, equipment,shaft element, bearing, and transducer. It is much easier to copy dataassociated with a particular machine, piece of equipment, shaft elementor bearing than it is to copy only at the lowest transducer level. Inembodiments, the system not only stores data in this hierarchicalfashion, but robustly supports the rapid copying of data using thesehierarchical templates. Similarity of elements at specific hierarchicallevels lends itself to effective data storage in hierarchical format.For example, so many machines have common elements such as motors,gearboxes, compressors, belts, fans, and so on. More specifically, manymotors can be easily classified as induction, DC, fixed or variablespeed. Many gearboxes can be grouped into commonly occurring groupingssuch as input/output, input pinion/intermediate pinion/output pinion,4-posters, and so on. Within a plant or company, there are many similartypes of equipment purchased and standardized on for both cost andmaintenance reasons. This results in an enormous overlapping of similartypes of equipment and, as a result, offers a great opportunity fortaking advantage of a hierarchical template approach.

Embodiments of the methods and systems disclosed herein may includesmart bands. Smart bands refer to any processed signal characteristicsderived from any dynamic input or group of inputs for the purposes ofanalyzing the data and achieving the correct diagnoses. Furthermore,smart bands may even include mini or relatively simple diagnoses for thepurposes of achieving a more robust and complex one. Historically, inthe field of mechanical vibration analysis, Alarm Bands have been usedto define spectral frequency bands of interest for the purposes ofanalyzing and/or trending significant vibration patterns. The Alarm Bandtypically consists of a spectral (amplitude plotted against frequency)region defined between a low and high frequency border. The amplitudebetween these borders is summed in the same manner for which an overallamplitude is calculated. A Smart Band is more flexible in that it notonly refers to a specific frequency band but can also refer to a groupof spectral peaks such as the harmonics of a single peak, a true-peaklevel or crest factor derived from a time waveform, an overall derivedfrom a vibration envelope spectrum or other specialized signal analysistechnique or a logical combination (AND, OR, XOR, etc.) of these signalattributes. In addition, a myriad assortment of other parametric data,including system load, motor voltage and phase information, bearingtemperature, flow rates, and the like, can likewise be used as the basisfor forming additional smart bands. In embodiments, Smart Band symptomsmay be used as building blocks for an expert system whose engine wouldutilize these inputs to derive diagnoses. Some of these mini-diagnosesmay then in turn be used as Smart-Band symptoms (smart bands can includeeven diagnoses) for more generalized diagnoses.

Embodiments of the methods and systems disclosed herein may include aneural net expert system using smart bands. Typical vibration analysisengines are rule-based (i.e., they use a list of expert rules which,when met, trigger specific diagnoses). In contrast, a neural approachutilizes the weighted triggering of multiple input stimuli into smalleranalytical engines or neurons which in turn feed a simplified weightedoutput to other neurons. The output of these neurons can be alsoclassified as smart bands which in turn feed other neurons. Thisproduces a more layered approach to expert diagnosing as opposed to theone-shot approach of a rule-based system. In embodiments, the expertsystem utilizes this neural approach using smart bands; however, it doesnot preclude rule-based diagnoses being reclassified as smart bands asfurther stimuli to be utilized by the expert system. From thispoint-of-view, it can be overviewed as a hybrid approach, although atthe highest level it is essentially neural.

Embodiments of the methods and systems disclosed herein may include useof database hierarchy in analysis smart band symptoms and diagnoses maybe assigned to various hierarchical database levels. For example, asmart band may be called “Looseness” at the bearing level, trigger“Looseness” at the equipment level, and trigger “Looseness” at themachine level. Another example would be having a smart band diagnosiscalled “Horizontal Plane Phase Flip” across a coupling and generate asmart band diagnosis of “Vertical Coupling Misalignment” at the machinelevel.

Embodiments of the methods and systems disclosed herein may includeexpert system GUIs. In embodiments, the system undertakes a graphicalapproach to defining smart bands and diagnoses for the expert system.The entry of symptoms, rules, or more generally smart bands for creatinga particular machine diagnosis, may be tedious and time consuming. Onemeans of making the process more expedient and efficient is to provide agraphical means by use of wiring. The proposed graphical interfaceconsists of four major components: a symptom parts bin, diagnoses bin,tools bin, and graphical wiring area (“GWA”). In embodiments, a symptomparts bin includes various spectral, waveform, envelope and any type ofsignal processing characteristic or grouping of characteristics such asa spectral peak, spectral harmonic, waveform true-peak, waveformcrest-factor, spectral alarm band, and so on. Each part may be assignedadditional properties. For example, a spectral peak part may be assigneda frequency or order (multiple) of running speed. Some parts may bepre-defined or user defined such as a 1×, 2×, 3× running speed, 1×, 2×,3× gear mesh, 1×, 2×, 3× blade pass, number of motor rotor bars×runningspeed, and so on.

In embodiments, the diagnoses bin includes various pre-defined as wellas user-defined diagnoses such as misalignment, imbalance, looseness,bearing faults, and so on. Like parts, diagnoses may also be used asparts for the purposes of building more complex diagnoses. Inembodiments, the tools bin includes logical operations such as AND, OR,XOR, etc. or other ways of combining the various parts listed above suchas Find Max, Find Min, Interpolate, Average, other StatisticalOperations, etc. In embodiments, a graphical wiring area includes partsfrom the parts bin or diagnoses from the diagnoses bin and may becombined using tools to create diagnoses. The various parts, tools anddiagnoses will be represented with icons which are simply graphicallywired together in the desired manner.

Embodiments of the methods and systems disclosed herein may include agraphical approach for back-calculation definition. In embodiments, theexpert system also provides the opportunity for the system to learn. Ifone already knows that a unique set of stimuli or smart bandscorresponds to a specific fault or diagnosis, then it is possible toback-calculate a set of coefficients that when applied to a future setof similar stimuli would arrive at the same diagnosis. In embodiments,if there are multiple sets of data, a best-fit approach may be used.Unlike the smart band GUI, this embodiment will self-generate a wiringdiagram. In embodiments, the user may tailor the back-propagationapproach settings and use a database browser to match specific sets ofdata with the desired diagnoses. In embodiments, the desired diagnosesmay be created or custom tailored with a smart band GUI. In embodiments,after that, a user may press the GENERATE button and a dynamic wiring ofthe symptom-to-diagnosis may appear on the screen as it works throughthe algorithms to achieve the best fit. In embodiments, when complete, avariety of statistics are presented which detail how well the mappingprocess proceeded. In some cases, no mapping may be achieved if, forexample, the input data was all zero or the wrong data (mistakenlyassigned) and so on. Embodiments of the methods and systems disclosedherein may include bearing analysis methods. In embodiments, bearinganalysis methods may be used in conjunction with a computer aided design(“CAD”), predictive deconvolution, minimum variance distortionlessresponse (“MVDR”) and spectrum sum-of-harmonics.

In recent years, there has been a strong drive to save power which hasresulted in an influx of variable frequency drives and variable speedmachinery. In embodiments, a bearing analysis method is provided. Inembodiments, torsional vibration detection and analysis is providedutilizing transitory signal analysis to provide an advanced torsionalvibration analysis for a more comprehensive way to diagnose machinerywhere torsional forces are relevant (such as machinery with rotatingcomponents). Due primarily to the decrease in cost of motor speedcontrol systems, as well as the increased cost and consciousness ofenergy-usage, it has become more economically justifiable to takeadvantage of the potentially vast energy savings of load control.Unfortunately, one frequently overlooked design aspect of this issue isthat of vibration. When a machine is designed to run at only one speed,it is far easier to design the physical structure accordingly so as toavoid mechanical resonances both structural and torsional, each of whichcan dramatically shorten the mechanical health of a machine. This wouldinclude such structural characteristics as the types of materials touse, their weight, stiffening member requirements and placement, bearingtypes, bearing location, base support constraints, etc. Even withmachines running at one speed, designing a structure so as to minimizevibration can prove a daunting task, potentially requiring computermodeling, finite-element analysis, and field testing. By throwingvariable speeds into the mix, in many cases, it becomes impossible todesign for all desirable speeds. The problem then becomes one ofminimization, e.g., by speed avoidance. This is why many modern motorcontrollers are typically programmed to skip or quickly pass throughspecific speed ranges or bands. Embodiments may include identifyingspeed ranges in a vibration monitoring system. Non-torsional, structuralresonances are typically fairly easy to detect using conventionalvibration analysis techniques. However, this is not the case fortorsion. One special area of current interest is the increased incidenceof torsional resonance problems, apparently due to the increasedtorsional stresses of speed change as well as the operation of equipmentat torsional resonance speeds. Unlike non-torsional structuralresonances which generally manifest their effect with dramaticallyincreased casing or external vibration, torsional resonances generallyshow no such effect. In the case of a shaft torsional resonance, thetwisting motion induced by the resonance may only be discernible bylooking for speed and/or phase changes. The current standard methodologyfor analyzing torsional vibration involves the use of specializedinstrumentation. Methods and systems disclosed herein allow analysis oftorsional vibration without such specialized instrumentation. This mayconsist of shutting the machine down and employing the use of straingauges and/or other special fixturing such as speed encoder platesand/or gears. Friction wheels are another alternative, but theytypically require manual implementation and a specialized analyst. Ingeneral, these techniques can be prohibitively expensive and/orinconvenient. An increasing prevalence of continuous vibrationmonitoring systems due to decreasing costs and increasing convenience(e.g., remote access) exists. In embodiments, there is an ability todiscern torsional speed and/or phase variations with just the vibrationsignal. In embodiments, transient analysis techniques may be utilized todistinguish torsionally induced vibrations from mere speed changes dueto process control. In embodiments, factors for discernment might focuson one or more of the following aspects: the rate of speed change due tovariable speed motor control would be relatively slow, sustained anddeliberate; torsional speed changes would tend to be short, impulsiveand not sustained; torsional speed changes would tend to be oscillatory,most likely decaying exponentially, process speed changes would not; andsmaller speed changes associated with torsion relative to the shaft'srotational speed which suggest that monitoring phase behavior would showthe quick or transient speed bursts in contrast to the slow phasechanges historically associated with ramping a machine's speed up ordown (as typified with Bode or Nyquist plots).

Embodiments of the methods and systems disclosed herein may includeimproved integration using both analog and digital methods. When asignal is digitally integrated using software, essentially the spectrallow-end frequency data has its amplitude multiplied by a function whichquickly blows up as it approaches zero and creates what is known in theindustry as a “ski-slope” effect. The amplitude of the ski-slope isessentially the noise floor of the instrument. The simple remedy forthis is the traditional hardware integrator, which can perform atsignal-to-noise ratios much greater than that of an already digitizedsignal. It can also limit the amplification factor to a reasonable levelso that multiplication by very large numbers is essentially prohibited.However, at high frequencies where the frequency becomes large, theoriginal amplitude which may be well above the noise floor is multipliedby a very small number (1/f) that plunges it well below the noise floor.The hardware integrator has a fixed noise floor that although low floordoes not scale down with the now lower amplitude high-frequency data. Incontrast, the same digital multiplication of a digitized high-frequencysignal also scales down the noise floor proportionally. In embodiments,hardware integration may be used below the point of unity gain where (ata value usually determined by units and/or desired signal to noise ratiobased on gain) and software integration may be used above the value ofunity gain to produce an ideal result. In embodiments, this integrationis performed in the frequency domain. In embodiments, the resultinghybrid data can then be transformed back into a waveform which should befar superior in signal-to-noise ratio when compared to either hardwareintegrated or software integrated data. In embodiments, the strengths ofhardware integration are used in conjunction with those of digitalsoftware integration to achieve the maximum signal-to-noise ratio. Inembodiments, the first order gradual hardware integrator high passfilter along with curve fitting allow some relatively low frequency datato get through while reducing or eliminating the noise, allowing veryuseful analytical data that steep filters kill to be salvaged.

Embodiments of the methods and systems disclosed herein may includeadaptive scheduling techniques for continuous monitoring. Continuousmonitoring is often performed with an up-front Mux whose purpose it isto select a few channels of data among many to feed the hardware signalprocessing, A/D, and processing components of a DAQ system. This is doneprimarily out of practical cost considerations. The tradeoff is that allof the points are not monitored continuously (although they may bemonitored to a lesser extent via alternative hardware methods). Inembodiments, multiple scheduling levels are provided. In embodiments, atthe lowest level, which is continuous for the most part, all of themeasurement points will be cycled through in round-robin fashion. Forexample, if it takes 30 seconds to acquire and process a measurementpoint and there are 30 points, then each point is serviced once every 15minutes; however, if a point should alarm by whatever criteria the userselects, its priority level can be increased so that it is serviced moreoften. As there can be multiple grades of severity for each alarm, socan there me multiple levels of priority with regards to monitoring. Inembodiments, more severe alarms will be monitored more frequently. Inembodiments, a number of additional high-level signal processingtechniques can be applied at less frequent intervals. Embodiments maytake advantage of the increased processing power of a PC and the PC cantemporarily suspend the round-robin route collection (with its multipletiers of collection) process and stream the required amount of data fora point of its choosing. Embodiments may include various advancedprocessing techniques such as envelope processing, wavelet analysis, aswell as many other signal processing techniques. In embodiments, afteracquisition of this data, the DAQ card set will continue with its routeat the point it was interrupted. In embodiments, various PC scheduleddata acquisitions will follow their own schedules which will be lessfrequency than the DAQ card route. They may be set up hourly, daily, bynumber of route cycles (for example, once every 10 cycles) and alsoincreased scheduling-wise based on their alarm severity priority or typeof measurement (e.g., motors may be monitored differently than fans).

Embodiments of the methods and systems disclosed herein may include dataacquisition parking features. In embodiments, a data acquisition boxused for route collection, real time analysis and in general as anacquisition instrument can be detached from its PC (tablet or otherwise)and powered by an external power supply or suitable battery. Inembodiments, the data collector still retains continuous monitoringcapability and its on-board firmware can implement dedicated monitoringfunctions for an extended period of time or can be controlled remotelyfor further analysis. Embodiments of the methods and systems disclosedherein may include extended statistical capabilities for continuousmonitoring.

Embodiments of the methods and systems disclosed herein may includeambient sensing plus local sensing plus vibration for analysis. Inembodiments, ambient environmental temperature and pressure, sensedtemperature and pressure may be combined with long/medium term vibrationanalysis for prediction of any of a range of conditions orcharacteristics. Variants may add infrared sensing, infraredthermography, ultrasound, and many other types of sensors and inputtypes in combination with vibration or with each other. Embodiments ofthe methods and systems disclosed herein may include a smart route. Inembodiments, the continuous monitoring system's software willadapt/adjust the data collection sequence based on statistics,analytics, data alarms and dynamic analysis. Typically, the route is setbased on the channels the sensors are attached to. In embodiments, withthe crosspoint switch, the Mux can combine any input Mux channels to the(e.g., eight) output channels. In embodiments, as channels go into alarmor the system identifies key deviations, it will pause the normal routeset in the software to gather specific simultaneous data, from thechannels sharing key statistical changes, for more advanced analysis.Embodiments include conducting a smart ODS or smart transfer function.

Embodiments of the methods and systems disclosed herein may includesmart ODS and one or more transfer functions. In embodiments, due to asystem's multiplexer and crosspoint switch, an ODS, a transfer function,or other special tests on all the vibration sensors attached to amachine/structure can be performed and show exactly how the machine'spoints are moving in relationship to each other. In embodiments, 40-50kHz and longer data lengths (e.g., at least one minute) may be streamed,which may reveal different information than what a normal ODS ortransfer function will show. In embodiments, the system will be able todetermine, based on the data/statistics/analytics to use, the smartroute feature that breaks from the standard route and conducts an ODSacross a machine, structure or multiple machines and structures thatmight show a correlation because the conditions/data directs it. Inembodiments, for the transfer functions there may be an impact hammerused on one channel and then compared against other vibration sensors onthe machine. In embodiments, the system may use the condition changessuch as load, speed, temperature or other changes in the machine orsystem to conduct the transfer function. In embodiments, differenttransfer functions may be compared to each other over time. Inembodiments, difference transfer functions may be strung together like amovie that may show how the machinery fault changes, such as a bearingthat could show how it moves through the four stages of bearing failureand so on. Embodiments of the methods and systems disclosed herein mayinclude a hierarchical Mux.

With reference to FIG. 8, the present disclosure generally includesdigitally collecting or streaming waveform data 2010 from a machine 2020whose operational speed can vary from relatively slow rotational oroscillational speeds to much higher speeds in different situations. Thewaveform data 2010, at least on one machine, may include data from asingle axis sensor 2030 mounted at an unchanging reference location 2040and from a three-axis sensor 2050 mounted at changing locations (orlocated at multiple locations), including location 2052. In embodiments,the waveform data 2010 can be vibration data obtained simultaneouslyfrom each sensor 2030, 2050 in a gap-five format for a duration ofmultiple minutes with maximum resolvable frequencies sufficiently largeto capture periodic and transient impact events. By way of this example,the waveform data 2010 can include vibration data that can be used tocreate an operational deflecting shape. It can also be used, as needed,to diagnose vibrations from which a machine repair solution can beprescribed.

In embodiments, the machine 2020 can further include a housing 2100 thatcan contain a drive motor 2110 that can drive a shaft 2120. The shaft2120 can be supported for rotation or oscillation by a set of bearings2130, such as including a first bearing 2140 and a second bearing 2150.A data collection module 2160 can connect to (or be resident on) themachine 2020. In one example, the data collection module 2160 can belocated and accessible through a cloud network facility 2170, cancollect the waveform data 2010 from the machine 2020, and deliver thewaveform data 2010 to a remote location. A working end 2180 of the driveshaft 2120 of the machine 2020 can drive a windmill, a fan, a pump, adrill, a gear system, a drive system, or other working element, as thetechniques described herein can apply to a wide range of machines,equipment, tools, or the like that include rotating or oscillatingelements. In other instances, a generator can be substituted for themotor 2110, and the working end of the drive shaft 2120 can directrotational energy to the generator to generate power, rather thanconsume it.

In embodiments, the waveform data 2010 can be obtained using apredetermined route format based on the layout of the machine 2020. Thewaveform data 2010 may include data from the single axis sensor 2030 andthe three-axis sensor 2050. The single-axis sensor 2030 can serve as areference probe with its one channel of data and can be fixed at theunchanging location 2040 on the machine under survey. The three-axissensor 2050 can serve as a tri-axial probe (e.g., three orthogonal axes)with its three channels of data and can be moved along a predetermineddiagnostic route format from one test point to the next test point. Inone example, both sensors 2030, 2050 can be mounted manually to themachine 2020 and can connect to a separate portable computer in certainservice examples. The reference probe can remain at one location whilethe user can move the tri-axial vibration probe along the predeterminedroute, such as from bearing-to-bearing on a machine. In this example,the user is instructed to locate the sensors at the predeterminedlocations to complete the survey (or portion thereof) of the machine.

With reference to FIG. 9, a portion of an exemplary machine 2200 isshown having a tri-axial sensor 2210 mounted to a location 2220associated with a motor bearing of the machine 2200 with an output shaft2230 and output member 2240 in accordance with the present disclosure.With reference to FIG. 10, an exemplary machine 2300 is shown having atri-axial sensor 2310 and a single-axis vibration sensor 2320 serving asthe reference sensor that is attached on the machine 2300 at anunchanging location for the duration of the vibration survey inaccordance with the present disclosure. The tri-axial sensor 2310 andthe single-axis vibration sensor 2320 can be connected to a datacollection system 2330.

In further examples, the sensors and data acquisition modules andequipment can be integral to, or resident on, the rotating machine. Byway of these examples, the machine can contain many single axis sensorsand many tri-axial sensors at predetermined locations. The sensors canbe originally installed equipment and provided by the original equipmentmanufacturer or installed at a different time in a retrofit application.The data collection module 2160, or the like, can select and use onesingle axis sensor and obtain data from it exclusively during thecollection of waveform data 2010 while moving to each of the tri-axialsensors. The data collection module 2160 can be resident on the machine2020 and/or connect via the cloud network facility 2170.

With reference to FIG. 8, the various embodiments include collecting thewaveform data 2010 by digitally recording locally, or streaming over,the cloud network facility 2170. The waveform data 2010 can be collectedso as to be gap-free with no interruptions and, in some respects, can besimilar to an analog recording of waveform data. The waveform data 2010from all of the channels can be collected for one to two minutesdepending on the rotating or oscillating speed of the machine beingmonitored. In embodiments, the data sampling rate can be at a relativelyhigh-sampling rate relative to the operating frequency of the machine2020.

In embodiments, a second reference sensor can be used, and a fifthchannel of data can be collected. As such, the single-axis sensor can bethe first channel and tri-axial vibration can occupy the second, thethird, and the fourth data channels. This second reference sensor, likethe first, can be a single axis sensor, such as an accelerometer. Inembodiments, the second reference sensor, like the first referencesensor, can remain in the same location on the machine for the entirevibration survey on that machine. The location of the first referencesensor (i.e., the single axis sensor) may be different than the locationof the second reference sensors (i.e., another single axis sensor). Incertain examples, the second reference sensor can be used when themachine has two shafts with different operating speeds, with the tworeference sensors being located on the two different shafts. Inaccordance with this example, further single-axis reference sensors canbe employed at additional but different unchanging locations associatedwith the rotating machine.

In embodiments, the waveform data can be transmitted electronically in agap-free free format at a significantly high rate of sampling for arelatively longer period of time. In one example, the period of time is60 seconds to 120 seconds. In another example, the rate of sampling is100 kHz with a maximum resolvable frequency (Fmax) of 40 kHz. It will beappreciated in light of this disclosure that the waveform data can beshown to approximate more closely some of the wealth of data availablefrom previous instances of analog recording of waveform data.

In embodiments, sampling, band selection, and filtering techniques canpermit one or more portions of a long stream of data (i.e., one to twominutes in duration) to be under sampled or over sampled to realizevarying effective sampling rates. To this end, interpolation anddecimation can be used to further realize varying effective samplingrates. For example, oversampling may be applied to frequency bands thatare proximal to rotational or oscillational operating speeds of thesampled machine, or to harmonics thereof, as vibration effects may tendto be more pronounced at those frequencies across the operating range ofthe machine. In embodiments, the digitally-sampled data set can bedecimated to produce a lower sampling rate. It will be appreciated inlight of the disclosure that decimate in this context can be theopposite of interpolate. In embodiments, decimating the data set caninclude first applying a low-pass filter to the digitally-sampled dataset and then undersampling the data set.

In one example, a sample waveform at 100 Hz can be undersampled at everytenth point of the digital waveform to produce an effective samplingrate of 10 Hz, but the remaining nine points of that portion of thewaveform are effectively discarded and not included in the modeling ofthe sample waveform. Moreover, this type of bare undersampling cancreate ghost frequencies due to the undersampling rate (i.e., 10 Hz)relative to the 100 Hz sample waveform.

Most hardware for analog-to-digital conversions uses a sample-and-holdcircuit that can charge up a capacitor for a given amount of time suchthat an average value of the waveform is determined over a specificchange in time. It will be appreciated in light of the disclosure thatthe value of the waveform over the specific change in time is not linearbut more similar to a cardinal sinusoidal (“sinc”) function; therefore,it can be shown that more emphasis can be placed on the waveform data atthe center of the sampling interval with exponential decay of thecardinal sinusoidal signal occurring from its center.

By way of the above example, the sample waveform at 100 Hz can behardware-sampled at 10 Hz and therefore each sampling point is averagedover 100 milliseconds (e.g., a signal sampled at 100 Hz can have eachpoint averaged over 10 milliseconds). In contrast to the effectivediscarding of nine out of the ten data points of the sampled waveform asdiscussed above, the present disclosure can include weighing adjacentdata. The adjacent data can refer to the sample points that werepreviously discarded and the one remaining point that was retained. Inone example, a low pass filter can average the adjacent sample datalinearly, i.e., determining the sum of every ten points and thendividing that sum by ten. In a further example, the adjacent data can beweighted with a sinc function. The process of weighting the originalwaveform with the sinc function can be referred to as an impulsefunction, or can be referred to in the time domain as a convolution.

The present disclosure can be applicable to not only digitizing awaveform signal based on a detected voltage, but can also be applicableto digitizing waveform signals based on current waveforms, vibrationwaveforms, and image processing signals including video signalrasterization. In one example, the resizing of a window on a computerscreen can be decimated, albeit in at least two directions. In thesefurther examples, it will be appreciated that undersampling by itselfcan be shown to be insufficient. To that end, oversampling or upsamplingby itself can similarly be shown to be insufficient, such thatinterpolation can be used like decimation but in lieu of onlyundersampling by itself.

It will be appreciated in light of the disclosure that interpolation inthis context can refer to first applying a low pass filter to thedigitally-sampled waveform data and then upsampling the waveform data.It will be appreciated in light of the disclosure that real-worldexamples can often require the use of use non-integer factors fordecimation or interpolation, or both. To that end, the presentdisclosure includes interpolating and decimating sequentially in orderto realize a non-integer factor rate for interpolating and decimating.In one example, interpolating and decimating sequentially can defineapplying a low-pass filter to the sample waveform, then interpolatingthe waveform after the low-pass filter, and then decimating the waveformafter the interpolation. In embodiments, the vibration data can belooped to purposely emulate conventional tape recorder loops, withdigital filtering techniques used with the effective splice tofacilitate longer analyses. It will be appreciated in light of thedisclosure that the above techniques do not preclude waveform, spectrum,and other types of analyses to be processed and displayed with a GUI ofthe user at the time of collection. It will be appreciated in light ofthe disclosure that newer systems can permit this functionality to beperformed in parallel to the high-performance collection of the rawwaveform data.

With respect to time of collection issues, it will be appreciated thatolder systems using the compromised approach of improving dataresolution, by collecting at different sampling rates and data lengths,do not in fact save as much time as expected. To that end, every timethe data acquisition hardware is stopped and started, latency issues canbe created, especially when there is hardware auto-scaling performed.The same can be true with respect to data retrieval of the routeinformation (i.e., test locations) that is often in a database formatand can be exceedingly slow. The storage of the raw data in bursts todisk (whether solid state or otherwise) can also be undesirably slow.

In contrast, the many embodiments include digitally streaming thewaveform data 2010, as disclosed herein, and also enjoying the benefitof needing to load the route parameter information while setting thedata acquisition hardware only once. Because the waveform data 2010 isstreamed to only one file, there is no need to open and close files, orswitch between loading and writing operations with the storage medium.It can be shown that the collection and storage of the waveform data2010, as described herein, can be shown to produce relatively moremeaningful data in significantly less time than the traditional batchdata acquisition approach. An example of this includes an electric motorabout which waveform data can be collected with a data length of 4Kpoints (i.e., 4,096) for sufficiently high resolution in order to, amongother things, distinguish electrical sideband frequencies. For fans orblowers, a reduced resolution of 1K (i.e., 1,024) can be used. Incertain instances, 1K can be the minimum waveform data lengthrequirement. The sampling rate can be 1,280 Hz and that equates to anFmax of 500 Hz. It will be appreciated in light of the disclosure thatoversampling by an industry standard factor of 2.56 can satisfy thenecessary two-times (2×) oversampling for the Nyquist Criterion withsome additional leeway that can accommodate anti-aliasingfilter-rolloff. The time to acquire this waveform data would be 1,024points at 1,280 hertz, which are 800 milliseconds.

To improve accuracy, the waveform data can be averaged. Eight averagescan be used with, for example, fifty percent overlap. This would extendthe time from 800 milliseconds to 3.6 seconds, which is equal to 800msec×8 averages×0.5 (overlap ratio)+0.5×800 msec (non-overlapped headand tail ends). After collection at Fmax=500 Hz waveform data, a highersampling rate can be used. In one example, ten times (10×) the previoussampling rate can be used and Fmax=10 kHz. By way of this example, eightaverages can be used with fifty percent (50%) overlap to collectwaveform data at this higher rate that can amount to a collection timeof 360 msec or 0.36 seconds. It will be appreciated in light of thedisclosure that it can be necessary to read the hardware collectionparameters for the higher sampling rate from the route list, as well aspermit hardware auto-scaling, or the resetting of other necessaryhardware collection parameters, or both. To that end, a few seconds oflatency can be added to accommodate the changes in sampling rate. Inother instances, introducing latency can accommodate hardwareautoscaling and changes to hardware collection parameters that can berequired when using the lower sampling rate disclosed herein. Inaddition to accommodating the change in sampling rate, additional timeis needed for reading the route point information from the database(i.e., where to monitor and where to monitor next), displaying the routeinformation, and processing the waveform data. Moreover, display of thewaveform data and/or associated spectra can also consume significanttime. In light of the above, 15 seconds to 20 seconds can elapse whileobtaining waveform data at each measurement point.

In further examples, additional sampling rates can be added but this canmake the total amount time for the vibration survey even longer becausetime adds up from changeover time from one sampling rate to another andfrom the time to obtain additional data at different sampling rate. Inone example, a lower sampling rate is used, such as a sampling rate of128 Hz where Fmax=50 Hz. By way of this example, the vibration surveywould, therefore, require an additional 36 seconds for the first set ofaveraged data at this sampling rate, in addition to others mentionedabove, and consequently the total time spent at each measurement pointincreases even more dramatically. Further embodiments include usingsimilar digital streaming of gap free waveform data as disclosed hereinfor use with wind turbines and other machines that can have relativelyslow speed rotating or oscillating systems. In many examples, thewaveform data collected can include long samples of data at a relativelyhigh-sampling rate. In one example, the sampling rate can be 100 kHz andthe sampling duration can be for two minutes on all of the channelsbeing recorded. In many examples, one channel can be for the single axisreference sensor and three more data channels can be for the tri-axialthree channel sensor. It will be appreciated in light of the disclosurethat the long data length can be shown to facilitate detection ofextremely low frequency phenomena. The long data length can also beshown to accommodate the inherent speed variability in wind turbineoperations. Additionally, the long data length can further be shown toprovide the opportunity for using numerous averages such as thosediscussed herein, to achieve very high spectral resolution, and to makefeasible tape loops for certain spectral analyses. Many multipleadvanced analytical techniques can now become available because suchtechniques can use the available long uninterrupted length of waveformdata in accordance with the present disclosure.

It will also be appreciated in light of the disclosure that thesimultaneous collection of waveform data from multiple channels canfacilitate performing transfer functions between multiple channels.Moreover, the simultaneous collection of waveform data from multiplechannels facilitates establishing phase relationships across the machineso that more sophisticated correlations can be utilized by relying onthe fact that the waveforms from each of the channels are collectedsimultaneously. In other examples, more channels in the data collectioncan be used to reduce the time it takes to complete the overallvibration survey by allowing for simultaneous acquisition of waveformdata from multiple sensors that otherwise would have to be acquired, ina subsequent fashion, moving sensor to sensor in the vibration survey.

The present disclosure includes the use of at least one of thesingle-axis reference probe on one of the channels to allow foracquisition of relative phase comparisons between channels. Thereference probe can be an accelerometer or other type of transducer thatis not moved and, therefore, fixed at an unchanging location during thevibration survey of one machine. Multiple reference probes can each bedeployed as at suitable locations fixed in place (i.e., at unchanginglocations) throughout the acquisition of vibration data during thevibration survey. In certain examples, up to seven reference probes canbe deployed depending on the capacity of the data collection module 2160or the like. Using transfer functions or similar techniques, therelative phases of all channels may be compared with one another at allselected frequencies. By keeping the one or more reference probes fixedat their unchanging locations while moving or monitoring the othertri-axial vibration sensors, it can be shown that the entire machine canbe mapped with regard to amplitude and relative phase. This can be shownto be true even when there are more measurement points than channels ofdata collection. With this information, an operating deflection shapecan be created that can show dynamic movements of the machine in 3D,which can provide an invaluable diagnostic tool. In embodiments, the oneor more reference probes can provide relative phase, rather thanabsolute phase. It will be appreciated in light of the disclosure thatrelative phase may not be as valuable absolute phase for some purposes,but the relative phase the information can still be shown to be veryuseful.

In embodiments, the sampling rates used during the vibration survey canbe digitally synchronized to predetermined operational frequencies thatcan relate to pertinent parameters of the machine such as rotating oroscillating speed. Doing this, permits extracting even more informationusing synchronized averaging techniques. It will be appreciated in lightof the disclosure that this can be done without the use of a key phasoror a reference pulse from a rotating shaft, which is usually notavailable for route collected data. As such, non-synchronous signals canbe removed from a complex signal without the need to deploy synchronousaveraging using the key phasor. This can be shown to be very powerfulwhen analyzing a particular pinion in a gearbox or generally applied toany component within a complicated mechanical mechanism. In manyinstances, the key phasor or the reference pulse is rarely availablewith route collected data, but the techniques disclosed herein canovercome this absence. In embodiments, there can be multiple shaftsrunning at different speeds within the machine being analyzed. Incertain instances, there can be a single-axis reference probe for eachshaft. In other instances, it is possible to relate the phase of oneshaft to another shaft using only one single axis reference probe on oneshaft at its unchanging location. In embodiments, variable speedequipment can be more readily analyzed with relatively longer durationof data relative to single speed equipment. The vibration survey can beconducted at several machine speeds within the same contiguous set ofvibration data using the same techniques disclosed herein. Thesetechniques can also permit the study of the change of the relationshipbetween vibration and the change of the rate of speed that was notavailable before.

In embodiments, there are numerous analytical techniques that can emergefrom because raw waveform data can be captured in a gap-free digitalformat as disclosed herein. The gap-free digital format can facilitatemany paths to analyze the waveform data in many ways after the fact toidentify specific problems. The vibration data collected in accordancewith the techniques disclosed herein can provide the analysis oftransient, semi-periodic and very low frequency phenomena. The waveformdata acquired in accordance with the present disclosure can containrelatively longer streams of raw gap-free waveform data that can beconveniently played back as needed, and on which many and variedsophisticated analytical techniques can be performed. A large number ofsuch techniques can provide for various forms of filtering to extractlow amplitude modulations from transient impact data that can beincluded in the relatively longer stream of raw gap-free waveform data.It will be appreciated in light of the disclosure that in past datacollection practices, these types of phenomena were typically lost bythe averaging process of the spectral processing algorithms because thegoal of the previous data acquisition module was purely periodicsignals; or these phenomena were lost to file size reductionmethodologies due to the fact that much of the content from an originalraw signal was typically discarded knowing it would not be used.

In embodiments, there is a method of monitoring vibration of a machinehaving at least one shaft supported by a set of bearings. The methodincludes monitoring a first data channel assigned to a single-axissensor at an unchanging location associated with the machine. The methodalso includes monitoring a second, third, and fourth data channelassigned to a three-axis sensor. The method further includes recordinggap-free digital waveform data simultaneously from all of the datachannels while the machine is in operation; and determining a change inrelative phase based on the digital waveform data. The method alsoincludes the tri-axial sensor being located at a plurality of positionsassociated with the machine while obtaining the digital waveform. Inembodiments, the second, third, and fourth channels are assignedtogether to a sequence of tri-axial sensors each located at differentpositions associated with the machine. In embodiments, the data isreceived from all of the sensors on all of their channelssimultaneously.

The method also includes determining an operating deflection shape basedon the change in relative phase information and the waveform data. Inembodiments, the unchanging location of the reference sensor is aposition associated with a shaft of the machine. In embodiments, thetri-axial sensors in the sequence of the tri-axial sensors are eachlocated at different positions and are each associated with differentbearings in the machine. In embodiments, the unchanging location is aposition associated with a shaft of the machine and, wherein, thetri-axial sensors in the sequence of the tri-axial sensors are eachlocated at different positions and are each associated with differentbearings that support the shaft in the machine. The various embodimentsinclude methods of sequentially monitoring vibration or similar processparameters and signals of a rotating or oscillating machine or analogousprocess machinery from a number of channels simultaneously, which can beknown as an ensemble. In various examples, the ensemble can include oneto eight channels. In further examples, an ensemble can represent alogical measurement grouping on the equipment being monitored whetherthose measurement locations are temporary for measurement, supplied bythe original equipment manufacturer, retrofit at a later date, or one ormore combinations thereof.

In one example, an ensemble can monitor bearing vibration in a singledirection. In a further example, an ensemble can monitor three differentdirections (e.g., orthogonal directions) using a tri-axial sensor. Inyet further examples, an ensemble can monitor four or more channelswhere the first channel can monitor a single axis vibration sensor, andthe second, the third, and the fourth channels can monitor each of thethree directions of the tri-axial sensor. In other examples, theensemble can be fixed to a group of adjacent bearings on the same pieceof equipment or an associated shaft. The various embodiments providemethods that include strategies for collecting waveform data fromvarious ensembles deployed in vibration studies or the like in arelatively more efficient manner. The methods also includesimultaneously monitoring of a reference channel assigned to anunchanging reference location associated with the ensemble monitoringthe machine. The cooperation with the reference channel can be shown tosupport a more complete correlation of the collected waveforms from theensembles. The reference sensor on the reference channel can be a singleaxis vibration sensor, or a phase reference sensor that can be triggeredby a reference location on a rotating shaft or the like. As disclosedherein, the methods can further include recording gap-free digitalwaveform data simultaneously from all of the channels of each ensembleat a relatively high rate of sampling so as to include all frequenciesdeemed necessary for the proper analysis of the machinery beingmonitored while it is in operation. The data from the ensembles can bestreamed gap-free to a storage medium for subsequent processing that canbe connected to a cloud network facility, a local data link, Bluetooth™connectivity, cellular data connectivity, or the like.

In embodiments, the methods disclosed herein include strategies forcollecting data from the various ensembles including digital signalprocessing techniques that can be subsequently applied to data from theensembles to emphasize or better isolate specific frequencies orwaveform phenomena. This can be in contrast with current methods thatcollect multiple sets of data at different sampling rates, or withdifferent hardware filtering configurations including integration, thatprovide relatively less post-processing flexibility because of thecommitment to these same (known as a priori hardware configurations).These same hardware configurations can also be shown to increase time ofthe vibration survey due to the latency delays associated withconfiguring the hardware for each independent test. In embodiments, themethods for collecting data from various ensembles include data markertechnology that can be used for classifying sections of streamed data ashomogenous and belonging to a specific ensemble. In one example, aclassification can be defined as operating speed. In doing so, amultitude of ensembles can be created from what conventional systemswould collect as only one. The many embodiments include post-processinganalytic techniques for comparing the relative phases of all thefrequencies of interest not only between each channel of the collectedensemble but also between all of the channels of all of the ensemblesbeing monitored, when applicable.

With reference to FIG. 12, the many embodiments include a first machine2400 having rotating or oscillating components 2410, or both, eachsupported by a set of bearings 2420 including a bearing pack 2422, abearing pack 2424, a bearing pack 2426, and more as needed. The firstmachine 2400 can be monitored by a first sensor ensemble 2450. The firstensemble 2450 can be configured to receive signals from sensorsoriginally installed (or added later) on the first machine 2400. Thesensors on the machine 2400 can include single-axis sensors 2460, suchas a single-axis sensor 2462, a single-axis sensor 2464, and more asneeded. In many examples, the single axis-sensors 2460 can be positionedin the machine 2400 at locations that allow for the sensing of one ofthe rotating or oscillating components 2410 of the machine 2400.

The machine 2400 can also have tri-axial (e.g., orthogonal axes) sensors2480, such as a tri-axial sensor 2482, a tri-axial sensor 2484, and moreas needed. In many examples, the tri-axial sensors 2480 can bepositioned in the machine 2400 at locations that allow for the sensingof one of each of the bearing packs in the sets of bearings 2420 that isassociated with the rotating or oscillating components of the machine2400. The machine 2400 can also have temperature sensors 2500, such as atemperature sensor 2502, a temperature sensor 2504, and more as needed.The machine 2400 can also have a tachometer sensor 2510 or more asneeded that each detail the RPMs of one of its rotating components. Byway of the above example, the first sensor ensemble 2450 can survey theabove sensors associated with the first machine 2400. To that end, thefirst ensemble 2450 can be configured to receive eight channels. Inother examples, the first sensor ensemble 2450 can be configured to havemore than eight channels, or less than eight channels as needed. In thisexample, the eight channels include two channels that can each monitor asingle-axis reference sensor signal and three channels that can monitora tri-axial sensor signal. The remaining three channels can monitor twotemperature signals and a signal from a tachometer. In one example, thefirst ensemble 2450 can monitor the single-axis sensor 2462, thesingle-axis sensor 2464, the tri-axial sensor 2482, the temperaturesensor 2502, the temperature sensor 2504, and the tachometer sensor 2510in accordance with the present disclosure. During a vibration survey onthe machine 2400, the first ensemble 2450 can first monitor thetri-axial sensor 2482 and then move next to the tri-axial sensor 2484.

After monitoring the tri-axial sensor 2484, the first ensemble 2450 canmonitor additional tri-axial sensors on the machine 2400 as needed andthat are part of the predetermined route list associated with thevibration survey of the machine 2400, in accordance with the presentdisclosure. During this vibration survey, the first ensemble 2450 cancontinually monitor the single-axis sensor 2462, the single-axis sensor2464, the two temperature sensors 2502, 2504, and the tachometer sensor2510 while the first ensemble 2450 can serially monitor the multipletri-axial sensors 2480 in the pre-determined route plan for thisvibration survey.

With reference to FIG. 12, the many embodiments include a second machine2600 having rotating or oscillating components 2610, or both, eachsupported by a set of bearings 2620 including a bearing pack 2622, abearing pack 2624, a bearing pack 2626, and more as needed. The secondmachine 2600 can be monitored by a second sensor ensemble 2650. Thesecond ensemble 2650 can be configured to receive signals from sensorsoriginally installed (or added later) on the second machine 2600. Thesensors on the machine 2600 can include single-axis sensors 2660, suchas a single-axis sensor 2662, a single-axis sensor 2664, and more asneeded. In many examples, the single axis-sensors 2660 can be positionedin the machine 2600 at locations that allow for the sensing of one ofthe rotating or oscillating components 2610 of the machine 2600.

The machine 2600 can also have tri-axial (e.g., orthogonal axes) sensors2680, such as a tri-axial sensor 2682, a tri-axial sensor 2684, atri-axial sensor 2686, a tri-axial sensor 2688, and more as needed. Inmany examples, the tri-axial sensors 2680 can be positioned in themachine 2600 at locations that allow for the sensing of one of each ofthe bearing packs in the sets of bearings 2620 that is associated withthe rotating or oscillating components of the machine 2600. The machine2600 can also have temperature sensors 2700, such as a temperaturesensor 2702, a temperature sensor 2704, and more as needed. The machine2600 can also have a tachometer sensor 2710 or more as needed that eachdetail the RPMs of one of its rotating components.

By way of the above example, the second sensor ensemble 2650 can surveythe above sensors associated with the second machine 2600. To that end,the second ensemble 2650 can be configured to receive eight channels. Inother examples, the second sensor ensemble 2650 can be configured tohave more than eight channels or less than eight channels as needed. Inthis example, the eight channels include one channel that can monitor asingle-axis reference sensor signal and six channels that can monitortwo tri-axial sensor signals. The remaining channel can monitor atemperature signal. In one example, the second ensemble 2650 can monitorthe single axis sensor 2662, the tri-axial sensor 2682, the tri-axialsensor 2684, and the temperature sensor 2702. During a vibration surveyon the machine 2600 in accordance with the present disclosure, thesecond ensemble 2650 can first monitor the tri-axial sensor 2682simultaneously with the tri-axial sensor 2684 and then move onto thetri-axial sensor 2686 simultaneously with the tri-axial sensor 2688.

After monitoring the tri-axial sensors 2680, the second ensemble 2650can monitor additional tri-axial sensors (in simultaneous pairs) on themachine 2600 as needed and that are part of the predetermined route listassociated with the vibration survey of the machine 2600 in accordancewith the present disclosure. During this vibration survey, the secondensemble 2650 can continually monitor the single-axis sensor 2662 at itsunchanging location and the temperature sensor 2702 while the secondensemble 2650 can serially monitor the multiple tri-axial sensors in thepre-determined route plan for this vibration survey.

With continuing reference to FIG. 12, the many embodiments include athird machine 2800 having rotating or oscillating components 2810, orboth, each supported by a set of bearings 2820 including a bearing pack2822, a bearing pack 2824, a bearing pack 2826, and more as needed. Thethird machine 2800 can be monitored by a third sensor ensemble 2850. Thethird ensemble 2850 can be configured with a single-axis sensor 2860,and two tri-axial (e.g., orthogonal axes) sensors 2880, 2882. In manyexamples, the single axis-sensor 2860 can be secured by the user on themachine 2800 at a location that allows for the sensing of one of therotating or oscillating components of the machine 2800. The tri-axialsensors 2880, 2882 can be also be located on the machine 2800 by theuser at locations that allow for the sensing of one of each of thebearings in the sets of bearings that each associated with the rotatingor oscillating components of the machine 2800. The third ensemble 2850can also include a temperature sensor 2900. The third ensemble 2850 andits sensors can be moved to other machines unlike the first and secondensembles 2450, 2650.

The many embodiments also include a fourth machine 2950 having rotatingor oscillating components 2960, or both, each supported by a set ofbearings 2970 including a bearing pack 2972, a bearing pack 2974, abearing pack 2976, and more as needed. The fourth machine 2950 can bealso monitored by the third sensor ensemble 2850 when the user moves itto the fourth machine 2950. The many embodiments also include a fifthmachine 3000 having rotating or oscillating components 3010, or both.The fifth machine 3000 may not be explicitly monitored by any sensor orany sensor ensembles in operation but it can create vibrations or otherimpulse energy of sufficient magnitude to be recorded in the dataassociated with any one of the machines 2400, 2600, 2800, 2950 under avibration survey.

The many embodiments include monitoring the first sensor ensemble 2450on the first machine 2400 through the predetermined route as disclosedherein. The many embodiments also include monitoring the second sensorensemble 2650 on the second machine 2600 through the predeterminedroute. The locations of machine 2400 being close to machine 2600 can beincluded in the contextual metadata of both vibration surveys. The thirdensemble 2850 can be moved between machine 2800, machine 2950, and othersuitable machines. The machine 3000 has no sensors onboard asconfigured, but could be monitored as needed by the third sensorensemble 2850. The machine 3000 and its operational characteristics canbe recorded in the metadata in relation to the vibration surveys on theother machines to note its contribution due to its proximity.

The many embodiments include hybrid database adaptation for harmonizingrelational metadata and streaming raw data formats. Unlike older systemsthat utilized traditional database structure for associating nameplateand operational parameters (sometimes deemed metadata) with individualdata measurements that are discrete and relatively simple, it will beappreciated in light of the disclosure that more modern systems cancollect relatively larger quantities of raw streaming data with highersampling rates and greater resolutions. At the same time, it will alsobe appreciated in light of the disclosure that the network of metadatawith which to link and obtain this raw data or correlate with this rawdata, or both, is expanding at ever-increasing rates.

In one example, a single overall vibration level can be collected aspart of a route or prescribed list of measurement points. This datacollected can then be associated with database measurement locationinformation for a point located on a surface of a bearing housing on aspecific piece of the machine adjacent to a coupling in a verticaldirection. Machinery analysis parameters relevant to the proper analysiscan be associated with the point located on the surface. Examples ofmachinery analysis parameters relevant to the proper analysis caninclude a running speed of a shaft passing through the measurement pointon the surface. Further examples of machinery analysis parametersrelevant to the proper analysis can include one of, or a combination of:running speeds of all component shafts for that piece of equipmentand/or machine, bearing types being analyzed such as sleeve or rollingelement bearings, the number of gear teeth on gears should there be agearbox, the number of poles in a motor, slip and line frequency of amotor, roller bearing element dimensions, number of fan blades, or thelike. Examples of machinery analysis parameters relevant to the properanalysis can further include machine operating conditions such as theload on the machines and whether load is expressed in percentage,wattage, air flow, head pressure, horsepower, and the like. Furtherexamples of machinery analysis parameters include information relevantto adjacent machines that might influence the data obtained during thevibration study.

It will be appreciated in light of the disclosure that the vast array ofequipment and machinery types can support many differentclassifications, each of which can be analyzed in distinctly differentways. For example, some machines, like screw compressors and hammermills, can be shown to run much noisier and can be expected to vibratesignificantly more than other machines. Machines known to vibrate moresignificantly can be shown to require a change in vibration levels thatcan be considered acceptable relative to quieter machines.

The present disclosure further includes hierarchical relationships foundin the vibrational data collected that can be used to support properanalysis of the data. One example of the hierarchical data includes theinterconnection of mechanical componentry such as a bearing beingmeasured in a vibration survey and the relationship between thatbearing, including how that bearing connects to a particular shaft onwhich is mounted a specific pinion within a particular gearbox, and therelationship between the shaft, the pinion, and the gearbox. Thehierarchical data can further include in what particular spot within amachinery gear train that the bearing being monitored is locatedrelative to other components in the machine. The hierarchical data canalso detail whether the bearing being measured in a machine is in closeproximity to another machine whose vibrations may affect what is beingmeasured in the machine that is the subject of the vibration study.

The analysis of the vibration data from the bearing or other componentsrelated to one another in the hierarchical data can use table lookups,searches for correlations between frequency patterns derived from theraw data, and specific frequencies from the metadata of the machine. Insome embodiments, the above can be stored in and retrieved from arelational database. In embodiments, National Instrument's TechnicalData Management Solution (TDMS) file format can be used. The TDMS fileformat can be optimized for streaming various types of measurement data(i.e., binary digital samples of waveforms), as well as also being ableto handle hierarchical metadata.

The many embodiments include a hybrid relational metadata-binary storageapproach (HRM-BSA). The HRM-BSA can include a structured query language(SQL) based relational database engine. The structured query languagebased relational database engine can also include a raw data engine thatcan be optimized for throughput and storage density for data that isflat and relatively structureless. It will be appreciated in light ofthe disclosure that benefits can be shown in the cooperation between thehierarchical metadata and the SQL relational database engine. In oneexample, marker technologies and pointer sign-posts can be used to makecorrelations between the raw database engine and the SQL relationaldatabase engine. Three examples of correlations between the raw databaseengine and the SQL relational database engine linkages include: (1)pointers from the SQL database to the raw data; (2) pointers from theancillary metadata tables or similar grouping of the raw data to the SQLdatabase; and (3) independent storage tables outside the domain ofeither the SQL database or raw data technologies.

With reference to FIG. 13, the present disclosure can include pointersfor Group 1 and Group 2 that can include associated filenames, pathinformation, table names, database key fields as employed with existingSQL database technologies that can be used to associate a specificdatabase segments or locations, asset properties to specific measurementraw data streams, records with associated time/date stamps, orassociated metadata such as operating parameters, panel conditions, andthe like. By way of this example, a plant 3200 can include machine one3202, machine two 3204, and many others in the plant 3200. The machineone 3202 can include a gearbox 3210, a motor 3212, and other elements.The machine two 3204 can include a motor 3220, and other elements. Manywaveforms 3230 including waveform 3240, waveform 3242, waveform 3244,and additional waveforms as needed can be acquired from the machines3202, 3204 in the plant 3200. The waveforms 3230 can be associated withthe local marker linking tables 3300 and the linking raw data tables3400. The machines 3202, 3204 and their elements can be associated withlinking tables having relational databases 3500. The linking tables rawdata tables 3400 and the linking tables having relational databases 3500can be associated with the linking tables with optional independentstorage tables 3600.

The present disclosure can include markers that can be applied to a timemark or a sample length within the raw waveform data. The markersgenerally fall into two categories: preset or dynamic. The presetmarkers can correlate to preset or existing operating conditions (e.g.,load, head pressure, air flow cubic feet per minute, ambienttemperature, RPMs, and the like.). These preset markers can be fed intothe data acquisition system directly. In certain instances, the presetmarkers can be collected on data channels in parallel with the waveformdata (e.g., waveforms for vibration, current, voltage, etc.).Alternatively, the values for the preset markers can be enteredmanually.

For dynamic markers such as trending data, it can be important tocompare similar data like comparing vibration amplitudes and patternswith a repeatable set of operating parameters. One example of thepresent disclosure includes one of the parallel channel inputs being akey phasor trigger pulse from an operating shaft that can provide RPMinformation at the instantaneous time of collection. In this example ofdynamic markers, sections of collected waveform data can be marked withappropriate speeds or speed ranges.

The present disclosure can also include dynamic markers that cancorrelate to data that can be derived from post processing and analyticsperformed on the sample waveform. In further embodiments, the dynamicmarkers can also correlate to post-collection derived parametersincluding RPMs, as well as other operationally derived metrics such asalarm conditions like a maximum RPM. In certain examples, many modernpieces of equipment that are candidates for a vibration survey with theportable data collection systems described herein do not includetachometer information. This can be true because it is not alwayspractical or cost justifiable to add a tachometer even though themeasurement of RPM can be of primary importance for the vibration surveyand analysis. It will be appreciated that for fixed speed machineryobtaining an accurate RPM measurement can be less important especiallywhen the approximate speed of the machine can be ascertainedbefore-hand; however, variable-speed drives are becoming more and moreprevalent. It will also be appreciated in light of the disclosure thatvarious signal processing techniques can permit the derivation of RPMfrom the raw data without the need for a dedicated tachometer signal.

In many embodiments, the RPM information can be used to mark segments ofthe raw waveform data over its collection history. Further embodimentsinclude techniques for collecting instrument data following a prescribedroute of a vibration study. The dynamic markers can enable analysis andtrending software to utilize multiple segments of the collectioninterval indicated by the markers (e.g., two minutes) as multiplehistorical collection ensembles, rather than just one as done inprevious systems where route collection systems would historically storedata for only one RPM setting. This could, in turn, be extended to anyother operational parameter such as load setting, ambient temperature,and the like, as previously described. The dynamic markers, however,that can be placed in a type of index file pointing to the raw datastream can classify portions of the stream in homogenous entities thatcan be more readily compared to previously collected portions of the rawdata stream

The many embodiments include the hybrid relational metadata-binarystorage approach that can use the best of pre-existing technologies forboth relational and raw data streams. In embodiments, the hybridrelational metadata-binary storage approach can marry them together witha variety of marker linkages. The marker linkages can permit rapidsearches through the relational metadata and can allow for moreefficient analyses of the raw data using conventional SQL techniqueswith pre-existing technology. This can be shown to permit utilization ofmany of the capabilities, linkages, compatibilities, and extensions thatconventional database technologies do not provide.

The marker linkages can also permit rapid and efficient storage of theraw data using conventional binary storage and data compressiontechniques. This can be shown to permit utilization of many of thecapabilities, linkages, compatibilities, and extensions thatconventional raw data technologies provide such as TMDS (NationalInstruments), UFF (Universal File Format such as UFF58), and the like.The marker linkages can further permit using the marker technology linkswhere a vastly richer set of data from the ensembles can be amassed inthe same collection time as more conventional systems. The richer set ofdata from the ensembles can store data snapshots associated withpredetermined collection criterion and the proposed system can derivemultiple snapshots from the collected data streams utilizing the markertechnology. In doing so, it can be shown that a relatively richeranalysis of the collected data can be achieved. One such benefit caninclude more trending points of vibration at a specific frequency ororder of running speed versus RPM, load, operating temperature, flowrates, and the like, which can be collected for a similar time relativeto what is spent collecting data with a conventional system.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals frommachines, elements of the machines and the environment of the machinesincluding heavy duty machines deployed at a local job site or atdistributed job sites under common control. The heavy-duty machines mayinclude earthmoving equipment, heavy duty on-road industrial vehicles,heavy duty off-road industrial vehicles, industrial machines deployed invarious settings such as turbines, turbomachinery, generators, pumps,pulley systems, manifold and valve systems, and the like. Inembodiments, heavy industrial machinery may also include earth-movingequipment, earth-compacting equipment, hauling equipment, hoistingequipment, conveying equipment, aggregate production equipment,equipment used in concrete construction, and piledriving equipment. Inexamples, earth moving equipment may include excavators, backhoes,loaders, bulldozers, skid steer loaders, trenchers, motor graders, motorscrapers, crawler loaders, and wheeled loading shovels. In examples,construction vehicles may include dumpers, tankers, tippers, andtrailers. In examples, material handling equipment may include cranes,conveyors, forklift, and hoists. In examples, construction equipment mayinclude tunnel and handling equipment, road rollers, concrete mixers,hot mix plants, road making machines (compactors), stone crashers,pavers, slurry seal machines, spraying and plastering machines, andheavy-duty pumps. Further examples of heavy industrial equipment mayinclude different systems such as implement traction, structure, powertrain, control, and information. Heavy industrial equipment may includemany different powertrains and combinations thereof to provide power forlocomotion and to also provide power to accessories and onboardfunctionality. In each of these examples, the platform 100 may deploythe local data collection system 102 into the environment 104 in whichthese machines, motors, pumps, and the like, operate and directlyconnected integrated into each of the machines, motors, pumps, and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals frommachines in operation and machines in being constructed such as turbineand generator sets like Siemens™ SGT6-5000F™ gas turbine, an SST-900™steam turbine, an SGen6-1000A™ generator, and an SGen6-100A™ generator,and the like. In embodiments, the local data collection system 102 maybe deployed to monitor steam turbines as they rotate in the currentscaused by hot water vapor that may be directed through the turbine butotherwise generated from a different source such as from gas-firedburners, nuclear cores, molten salt loops and the like. In thesesystems, the local data collection system 102 may monitor the turbinesand the water or other fluids in a closed loop cycle in which watercondenses and is then heated until it evaporates again. The local datacollection system 102 may monitor the steam turbines separately from thefuel source deployed to heat the water to steam. In examples, workingtemperatures of steam turbines may be between 500 and 650° C. In manyembodiments, an array of steam turbines may be arranged and configuredfor high, medium, and low pressure, so they may optimally convert therespective steam pressure into rotational movement.

The local data collection system 102 may also be deployed in a gasturbines arrangement and therefore not only monitor the turbine inoperation but also monitor the hot combustion gases feed into theturbine that may be in excess of 1,500° C. Because these gases are muchhotter than those in steam turbines, the blades may be cooled with airthat may flow out of small openings to create a protective film orboundary layer between the exhaust gases and the blades. Thistemperature profile may be monitored by the local data collection system102. Gas turbine engines, unlike typical steam turbines, include acompressor, a combustion chamber, and a turbine all of which arejournaled for rotation with a rotating shaft. The construction andoperation of each of these components may be monitored by the local datacollection system 102.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from waterturbines serving as rotary engines that may harvest energy from movingwater and are used for electric power generation. The type of waterturbine or hydro-power selected for a project may be based on the heightof standing water, often referred to as head, and the flow (or volume ofwater) at the site. In this example, a generator may be placed at thetop of a shaft that connects to the water turbine. As the turbinecatches the naturally moving water in its blade and rotates, the turbinesends rotational power to the generator to generate electrical energy.In doing so, the platform 100 may monitor signals from the generators,the turbines, the local water system, flow controls such as dam windowsand sluices. Moreover, the platform 100 may monitor local conditions onthe electric grid including load, predicted demand, frequency response,and the like, and include such information in the monitoring and controldeployed by platform 100 in these hydroelectric settings.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromenergy production environments, such as thermal, nuclear, geothermal,chemical, biomass, carbon-based fuels, hybrid-renewable energy plants,and the like. Many of these plants may use multiple forms of energyharvesting equipment like wind turbines, hydro turbines, and steamturbines powered by heat from nuclear, gas-fired, solar, and molten saltheat sources. In embodiments, elements in such systems may includetransmission lines, heat exchangers, desulphurization scrubbers, pumps,coolers, recuperators, chillers, and the like. In embodiments, certainimplementations of turbomachinery, turbines, scroll compressors, and thelike may be configured in arrayed control so as to monitor largefacilities creating electricity for consumption, providingrefrigeration, creating steam for local manufacture and heating, and thelike, and that arrayed control platforms may be provided by the providerof the industrial equipment such as Honeywell and their Experion™ PKSplatform. In embodiments, the platform 100 may specifically communicatewith and integrate the local manufacturer-specific controls and mayallow equipment from one manufacturer to communicate with otherequipment. Moreover, the platform 100 provides allows for the local datacollection system 102 to collect information across systems from manydifferent manufacturers. In embodiments, the platform 100 may includethe local data collection system 102 deployed in the environment 104 tomonitor signals from marine industrial equipment, marine diesel engines,shipbuilding, oil and gas plants, refineries, petrochemical plant,ballast water treatment solutions, marine pumps and turbines, and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from heavyindustrial equipment and processes including monitoring one or moresensors. By way of this example, sensors may be devices that may be usedto detect or respond to some type of input from a physical environment,such as an electrical, heat, or optical signal. In embodiments, thelocal data collection system 102 may include multiple sensors such as,without limitation, a temperature sensor, a pressure sensor, a torquesensor, a flow sensor, a heat sensor, a smoke sensor, an arc sensor, aradiation sensor, a position sensor, an acceleration sensor, a strainsensor, a pressure cycle sensor, a pressure sensor, an air temperaturesensor, and the like. The torque sensor may encompass a magnetic twistangle sensor. In one example, the torque and speed sensors in the localdata collection system 102 may be similar to those discussed in U.S.Pat. No. 8,352,149 to Meachem, issued 8 Jan. 2013 and herebyincorporated by reference as if fully set forth herein. In embodiments,one or more sensors may be provided such as a tactile sensor, abiosensor, a chemical sensor, an image sensor, a humidity sensor, aninertial sensor, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromsensors that may provide signals for fault detection including excessivevibration, incorrect material, incorrect material properties, truenessto the proper size, trueness to the proper shape, proper weight,trueness to balance. Additional fault sensors include those forinventory control and for inspections such as to confirm that parts arepackaged to plan, parts are to tolerance in a plan, occurrence ofpackaging damage or stress, and sensors that may indicate the occurrenceof shock or damage in transit. Additional fault sensors may includedetection of the lack of lubrication, over lubrication, the need forcleaning of the sensor detection window, the need for maintenance due tolow lubrication, the need for maintenance due to blocking or reducedflow in a lubrication region, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 that includes aircraftoperations and manufacture including monitoring signals from sensors forspecialized applications such as sensors used in an aircraft's Attitudeand Heading Reference System (AHRS), such as gyroscopes, accelerometers,and magnetometers. In embodiments, the platform 100 may include thelocal data collection system 102 deployed in the environment 104 tomonitor signals from image sensors such as semiconductor charge coupleddevices (CCDs), active pixel sensors, in complementarymetal-oxide-semiconductor (CMOS) or N-type metal-oxide-semiconductor(NMOS, Live MOS) technologies. In embodiments, the platform 100 mayinclude the local data collection system 102 deployed in the environment104 to monitor signals from sensors such as an infrared (IR) sensor, anultraviolet (UV) sensor, a touch sensor, a proximity sensor, and thelike. In embodiments, the platform 100 may include the local datacollection system 102 deployed in the environment 104 to monitor signalsfrom sensors configured for optical character recognition (OCR), readingbarcodes, detecting surface acoustic waves, detecting transponders,communicating with home automation systems, medical diagnostics, healthmonitoring, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromsensors such as a Micro-Electro-Mechanical Systems (MEMS) sensor, suchas ST Microelectronic's™ LSM303AH smart MEMS sensor, which may includean ultra-low-power high-performance system-in-package featuring a 3Ddigital linear acceleration sensor and a 3D digital magnetic sensor.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromadditional large machines such as turbines, windmills, industrialvehicles, robots, and the like. These large mechanical machines includemultiple components and elements providing multiple subsystems on eachmachine. To that end, the platform 100 may include the local datacollection system 102 deployed in the environment 104 to monitor signalsfrom individual elements such as axles, bearings, belts, buckets, gears,shafts, gear boxes, cams, carriages, camshafts, clutches, brakes, drums,dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals,sockets, sleeves, valves, wheels, actuators, motors, servomotor, and thelike. Many of the machines and their elements may include servomotors.The local data collection system 102 may monitor the motor, the rotaryencoder, and the potentiometer of the servomechanism to providethree-dimensional detail of position, placement, and progress ofindustrial processes.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from geardrives, powertrains, transfer cases, multispeed axles, transmissions,direct drives, chain drives, belt-drives, shaft-drives, magnetic drives,and similar meshing mechanical drives. In embodiments, the platform 100may include the local data collection system 102 deployed in theenvironment 104 to monitor signals from fault conditions of industrialmachines that may include overheating, noise, grinding gears, lockedgears, excessive vibration, wobbling, under-inflation, over-inflation,and the like. Operation faults, maintenance indicators, and interactionsfrom other machines may cause maintenance or operational issues mayoccur during operation, during installation, and during maintenance. Thefaults may occur in the mechanisms of the industrial machines but mayalso occur in infrastructure that supports the machine such as itswiring and local installation platforms. In embodiments, the largeindustrial machines may face different types of fault conditions such asoverheating, noise, grinding gears, excessive vibration of machineparts, fan vibration problems, problems with large industrial machinesrotating parts.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromindustrial machinery including failures that may be caused by prematurebearing failure that may occur due to contamination or loss of bearinglubricant. In another example, a mechanical defect such as misalignmentof bearings may occur. Many factors may contribute to the failure suchas metal fatigue, therefore, the local data collection system 102 maymonitor cycles and local stresses. By way of this example, the platform100 may monitor the incorrect operation of machine parts, lack ofmaintenance and servicing of parts, corrosion of vital machine parts,such as couplings or gearboxes, misalignment of machine parts, and thelike. Though the fault occurrences cannot be completely stopped, manyindustrial breakdowns may be mitigated to reduce operational andfinancial losses. The platform 100 provides real-time monitoring andpredictive maintenance in many industrial environments wherein it hasbeen shown to present a cost-savings over regularly-scheduledmaintenance processes that replace parts according to a rigid expirationof time and not actual load and wear and tear on the element or machine.To that end, the platform 10 may provide reminders of, or perform some,preventive measures such as adhering to operating manual and modeinstructions for machines, proper lubrication, and maintenance ofmachine parts, minimizing or eliminating overrun of machines beyondtheir defined capacities, replacement of worn but still functional partsas needed, properly training the personnel for machine use, and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor multiple signalsthat may be carried by a plurality of physical, electronic, and symbolicformats or signals. The platform 100 may employ signal processingincluding a plurality of mathematical, statistical, computational,heuristic, and linguistic representations and processing of signals anda plurality of operations needed for extraction of useful informationfrom signal processing operations such as techniques for representation,modeling, analysis, synthesis, sensing, acquisition, and extraction ofinformation from signals. In examples, signal processing may beperformed using a plurality of techniques, including but not limited totransformations, spectral estimations, statistical operations,probabilistic and stochastic operations, numerical theory analysis, datamining, and the like. The processing of various types of signals formsthe basis of many electrical or computational process. As a result,signal processing applies to almost all disciplines and applications inthe industrial environment such as audio and video processing, imageprocessing, wireless communications, process control, industrialautomation, financial systems, feature extraction, quality improvementssuch as noise reduction, image enhancement, and the like. Signalprocessing for images may include pattern recognition for manufacturinginspections, quality inspection, and automated operational inspectionand maintenance. The platform 100 may employ many pattern recognitiontechniques including those that may classify input data into classesbased on key features with the objective of recognizing patterns orregularities in data. The platform 100 may also implement patternrecognition processes with machine learning operations and may be usedin applications such as computer vision, speech and text processing,radar processing, handwriting recognition, CAD systems, and the like.The platform 100 may employ supervised classification and unsupervisedclassification. The supervised learning classification algorithms may bebased to create classifiers for image or pattern recognition, based ontraining data obtained from different object classes. The unsupervisedlearning classification algorithms may operate by finding hiddenstructures in unlabeled data using advanced analysis techniques such assegmentation and clustering. For example, some of the analysistechniques used in unsupervised learning may include K-means clustering,Gaussian mixture models, Hidden Markov models, and the like. Thealgorithms used in supervised and unsupervised learning methods ofpattern recognition enable the use of pattern recognition in varioushigh precision applications. The platform 100 may use patternrecognition in face detection related applications such as securitysystems, tracking, sports related applications, fingerprint analysis,medical and forensic applications, navigation and guidance systems,vehicle tracking, public infrastructure systems such as transportsystems, license plate monitoring, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 using machine learning toenable derivation-based learning outcomes from computers without theneed to program them. The platform 100 may, therefore, learn from andmake decisions on a set of data, by making data-driven predictions andadapting according to the set of data. In embodiments, machine learningmay involve performing a plurality of machine learning tasks by machinelearning systems, such as supervised learning, unsupervised learning,and reinforcement learning. Supervised learning may include presenting aset of example inputs and desired outputs to the machine learningsystems. Unsupervised learning may include the learning algorithm itselfstructuring its input by methods such as pattern detection and/orfeature learning. Reinforcement learning may include the machinelearning systems performing in a dynamic environment and then providingfeedback about correct and incorrect decisions. In examples, machinelearning may include a plurality of other tasks based on an output ofthe machine learning system. In examples, the tasks may also beclassified as machine learning problems such as classification,regression, clustering, density estimation, dimensionality reduction,anomaly detection, and the like. In examples, machine learning mayinclude a plurality of mathematical and statistical techniques. Inexamples, the many types of machine learning algorithms may includedecision tree based learning, association rule learning, deep learning,artificial neural networks, genetic learning algorithms, inductive logicprogramming, support vector machines (SVMs), Bayesian network,reinforcement learning, representation learning, rule-based machinelearning, sparse dictionary learning, similarity and metric learning,learning classifier systems (LCS), logistic regression, random forest,K-Means, gradient boost and adaboost, K-nearest neighbors (KNN), apriori algorithms, and the like. In embodiments, certain machinelearning algorithms may be used (such as genetic algorithms defined forsolving both constrained and unconstrained optimization problems thatmay be based on natural selection, the process that drives biologicalevolution). By way of this example, genetic algorithms may be deployedto solve a variety of optimization problems that are not well suited forstandard optimization algorithms, including problems in which theobjective functions are discontinuous, not differentiable, stochastic,or highly nonlinear. In an example, the genetic algorithm may be used toaddress problems of mixed integer programming, where some componentsrestricted to being integer-valued. Genetic algorithms and machinelearning techniques and systems may be used in computationalintelligence systems, computer vision, Natural Language Processing(NLP), recommender systems, reinforcement learning, building graphicalmodels, and the like. By way of this example, the machine learningsystems may be used to perform intelligent computing based control andbe responsive to tasks in a wide variety of systems (such as interactivewebsites and portals, brain-machine interfaces, online security andfraud detection systems, medical applications such as diagnosis andtherapy assistance systems, classification of DNA sequences, and thelike). In examples, machine learning systems may be used in advancedcomputing applications (such as online advertising, natural languageprocessing, robotics, search engines, software engineering, speech andhandwriting recognition, pattern matching, game playing, computationalanatomy, bioinformatics systems and the like). In an example, machinelearning may also be used in financial and marketing systems (such asfor user behavior analytics, online advertising, economic estimations,financial market analysis, and the like).

Additional details are provided below in connection with the methods,systems, devices, and components depicted in connection with FIGS. 1through 6. In embodiments, methods and systems are disclosed herein forcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors. For example, data streams from vibration,pressure, temperature, accelerometer, magnetic, electrical field, andother analog sensors may be multiplexed or otherwise fused, relayed overa network, and fed into a cloud-based machine learning facility, whichmay employ one or more models relating to an operating characteristic ofan industrial machine, an industrial process, or a component or elementthereof. A model may be created by a human who has experience with theindustrial environment and may be associated with a training data set(such as models created by human analysis or machine analysis of datathat is collected by the sensors in the environment, or sensors in othersimilar environments. The learning machine may then operate on otherdata, initially using a set of rules or elements of a model, such as toprovide a variety of outputs, such as classification of data into types,recognition of certain patterns (such as those indicating the presenceof faults, orthoses indicating operating conditions, such as fuelefficiency, energy production, or the like). The machine learningfacility may take feedback, such as one or more inputs or measures ofsuccess, such that it may train, or improve, its initial model (such asimprovements by adjusting weights, rules, parameters, or the like, basedon the feedback). For example, a model of fuel consumption by anindustrial machine may include physical model parameters thatcharacterize weights, motion, resistance, momentum, inertia,acceleration, and other factors that indicate consumption, and chemicalmodel parameters (such as those that predict energy produced and/orconsumed e.g., such as through combustion, through chemical reactions inbattery charging and discharging, and the like). The model may berefined by feeding in data from sensors disposed in the environment of amachine, in the machine, and the like, as well as data indicating actualfuel consumption, so that the machine can provide increasingly accurate,sensor-based, estimates of fuel consumption and can also provide outputthat indicate what changes can be made to increase fuel consumption(such as changing operation parameters of the machine or changing otherelements of the environment, such as the ambient temperature, theoperation of a nearby machine, or the like). For example, if a resonanceeffect between two machines is adversely affecting one of them, themodel may account for this and automatically provide an output thatresults in changing the operation of one of the machines (such as toreduce the resonance, to increase fuel efficiency of one or bothmachines). By continuously adjusting parameters to cause outputs tomatch actual conditions, the machine learning facility may self-organizeto provide a highly accurate model of the conditions of an environment(such as for predicting faults, optimizing operational parameters, andthe like). This may be used to increase fuel efficiency, to reduce wear,to increase output, to increase operating life, to avoid faultconditions, and for many other purposes.

FIG. 14 illustrates components and interactions of a data collectionarchitecture involving the application of cognitive and machine learningsystems to data collection and processing. Referring to FIG. 14, a datacollection system 102 may be disposed in an environment (such as anindustrial environment where one or more complex systems, such aselectro-mechanical systems and machines are manufactured, assembled, oroperated). The data collection system 102 may include onboard sensorsand may take input, such as through one or more input interfaces orports 4008, from one or more sensors (such as analog or digital sensorsof any type disclosed herein) and from one or more input sources 116(such as sources that may be available through Wi-Fi, Bluetooth, NFC, orother local network connections or over the Internet). Sensors may becombined and multiplexed (such as with one or more multiplexers 4002).Data may be cached or buffered in a cache/buffer 4022 and made availableto external systems, such as a remote host processing system 112 asdescribed elsewhere in this disclosure (which may include an extensiveprocessing architecture 4024, including any of the elements described inconnection with other embodiments described throughout this disclosureand in the Figure), though one or more output interfaces and ports 4010(which may in embodiments be separate from or the same as the inputinterfaces and ports 4008). The data collection system 102 may beconfigured to take input from a host processing system 112, such asinput from an analytic system 4018, which may operate on data from thedata collection system 102 and data from other input sources 116 toprovide analytic results, which in turn may be provided as a learningfeedback input 4012 to the data collection system, such as to assist inconfiguration and operation of the data collection system 102.

Combination of inputs (including selection of what sensors or inputsources to turn “on” or “off”) may be performed under the control ofmachine-based intelligence, such as using a local cognitive inputselection system 4004, an optionally remote cognitive input selectionsystem 4114, or a combination of the two. The cognitive input selectionsystems 4004, 4014 may use intelligence and machine learningcapabilities described elsewhere in this disclosure, such as usingdetected conditions (such as conditions informed by the input sources116 or sensors), state information (including state informationdetermined by a machine state recognition system 4020 that may determinea state), such as relating to an operational state, an environmentalstate, a state within a known process or workflow, a state involving afault or diagnostic condition, or many others. This may includeoptimization of input selection and configuration based on learningfeedback from the learning feedback system 4012, which may includeproviding training data (such as from the host processing system 112 orfrom other data collection systems 102 either directly or from the host112) and may include providing feedback metrics, such as success metricscalculated within the analytic system 4018 of the host processing system112. For example, if a data stream consisting of a particularcombination of sensors and inputs yields positive results in a given setof conditions (such as providing improved pattern recognition, improvedprediction, improved diagnosis, improved yield, improved return oninvestment, improved efficiency, or the like), then metrics relating tosuch results from the analytic system 4018 can be provided via thelearning feedback system 4012 to the cognitive input selection systems4004, 4014 to help configure future data collection to select thatcombination in those conditions (allowing other input sources to bede-selected, such as by powering down the other sensors). Inembodiments, selection and de-selection of sensor combinations, undercontrol of one or more of the cognitive input selection systems 4004,may occur with automated variation, such as using genetic programmingtechniques, based on learning feedback 4012, such as from the analyticsystem 4018, effective combinations for a given state or set ofconditions are promoted, and less effective combinations are demoted,resulting in progressive optimization and adaptation of the local datacollection system to each unique environment. Thus, an automaticallyadapting, multi-sensor data collection system is provided, wherecognitive input selection is used (with feedback) to improve theeffectiveness, efficiency, or other performance parameters of the datacollection system within its particular environment. Performanceparameters may relate to overall system metrics (such as financialyields, process optimization results, energy production or usage, andthe like), analytic metrics (such as success in recognizing patterns,making predictions, classifying data, or the like), and local systemmetrics (such as bandwidth utilization, storage utilization, powerconsumption, and the like). In embodiments, the analytic system 4018,the state system 4020 and the cognitive input selection system 4114 of ahost may take data from multiple data collection systems 102, such thatoptimization (including of input selection) may be undertaken throughcoordinated operation of multiple systems 102. For example, thecognitive input selection system 4114 may understand that if one datacollection system 102 is already collecting vibration data for anX-axis, the X-axis vibration sensor for the other data collection systemmight be turned off, in favor of getting Y-axis data from the other datacollector 102. Thus, through coordinated collection by the hostcognitive input selection system 4114, the activity of multiplecollectors 102, across a host of different sensors, can provide for arich data set for the host processing system 112, without wastingenergy, bandwidth, storage space, or the like. As noted above,optimization may be based on overall system success metrics, analyticsuccess metrics, and local system metrics, or a combination of theabove.

Methods and systems are disclosed herein for cloud-based, machinepattern analysis of state information from multiple industrial sensorsto provide anticipated state information for an industrial system. Inembodiments, machine learning may take advantage of a state machine,such as tracking states of multiple analog and/or digital sensors,feeding the states into a pattern analysis facility, and determininganticipated states of the industrial system based on historical dataabout sequences of state information. For example, where a temperaturestate of an industrial machine exceeds a certain threshold and isfollowed by a fault condition, such as breaking down of a set ofbearings, that temperature state may be tracked by a pattern recognizer,which may produce an output data structure indicating an anticipatedbearing fault state (whenever an input state of a high temperature isrecognized). A wide range of measurement values and anticipated statesmay be managed by a state machine, relating to temperature, pressure,vibration, acceleration, momentum, inertia, friction, heat, heat flux,galvanic states, magnetic field states, electrical field states,capacitance states, charge and discharge states, motion, position, andmany others. States may comprise combined states, where a data structureincludes a series of states, each of which is represented by a place ina byte-like data structure. For example, an industrial machine may becharacterized by a genetic structure, such as one that providespressure, temperature, vibration, and acoustic data, the measurement ofwhich takes one place in the data structure, so that the combined statecan be operated on as a byte-like structure, such as a structure forcompactly characterizing the current combined state of the machine orenvironment, or compactly characterizing the anticipated state. Thisbyte-like structure can be used by a state machine for machine learning,such as pattern recognition that operates on the structure to determinepatterns that reflect combined effects of multiple conditions. A widevariety of such structure can be tracked and used, such as in machinelearning, representing various combinations, of various length, of thedifferent elements that can be sensed in an industrial environment. Inembodiments, byte-like structures can be used in a genetic programmingtechnique, such as by substituting different types of data, or data fromvarying sources, and tracking outcomes over time, so that one or morefavorable structures emerges based on the success of those structureswhen used in real world situations, such as indicating successfulpredictions of anticipated states, or achievement of success operationaloutcomes, such as increased efficiency, successful routing ofinformation, achieving increased profits, or the like. That is, byvarying what data types and sources are used in byte-like structuresthat are used for machine optimization over time, a geneticprogramming-based machine learning facility can “evolve” a set of datastructures, consisting of a favorable mix of data types (e.g., pressure,temperature, and vibration), from a favorable mix of data sources (e.g.,temperature is derived from sensor X, while vibration comes from sensorY), for a given purpose. Different desired outcomes may result indifferent data structures that are best adapted to support effectiveachievement of those outcomes over time with application of machinelearning and promotion of structures with favorable results for thedesired outcome in question by genetic programming. The promoted datastructures may provide compact, efficient data for various activities asdescribed throughout this disclosure, including being stored in datapools (which may be optimized by storing favorable data structures thatprovide the best operational results for a given environment), beingpresented in data marketplaces (such as being presented as the mosteffective structures for a given purpose), and the like.

In embodiments, a platform is provided having cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem. In embodiments, the host processing system 112, such as disposedin the cloud, may include the state system 4020, which may be used toinfer or calculate a current state or to determine an anticipated futurestate relating to the data collection system 102 or some aspect of theenvironment in which the data collection system 102 is disposed, such asthe state of a machine, a component, a workflow, a process, an event(e.g., whether the event has occurred), an object, a person, acondition, a function, or the like Maintaining state information allowsthe host processing system 112 to undertake analysis, such as in one ormore analytic systems 4018, to determine contextual information, toapply semantic and conditional logic, and perform many other functionsas enabled by the processing architecture 4024 described throughout thisdisclosure.

In embodiments, a platform is provided having cloud-based policyautomation engine for IoT, with creation, deployment, and management ofIoT devices. In embodiments, the platform 100 includes (or is integratedwith, or included in) the host processing system 112, such as on a cloudplatform, a policy automation engine 4032 for automating creation,deployment, and management of policies to IoT devices. Polices, whichmay include access policies, network usage policies, storage usagepolicies, bandwidth usage policies, device connection policies, securitypolicies, rule-based policies, role-based polices, and others, may berequired to govern the use of IoT devices. For example, as IoT devicesmay have many different network and data communications to otherdevices, policies may be needed to indicate to what devices a givendevice can connect, what data can be passed on, and what data can bereceived. As billions of devices with countless potential connectionsare expected to be deployed in the near future, it becomes impossiblefor humans to configure policies for IoT devices on aconnection-by-connection basis. Accordingly, an intelligent policyautomation engine 4032 may include cognitive features for creating,configuring, and managing policies. The policy automation engine 4032may consume information about possible policies, such as from a policydatabase or library, which may include one or more public sources ofavailable policies. These may be written in one or more conventionalpolicy languages or scripts. The policy automation engine 4032 may applythe policies according to one or more models, such as based on thecharacteristics of a given device, machine, or environment. For example,a large machine, such as a machine for power generation, may include apolicy that only a verifiably local controller can change certainparameters of the power generation, thereby avoiding a remote “takeover”by a hacker. This may be accomplished in turn by automatically findingand applying security policies that bar connection of the controlinfrastructure of the machine to the Internet, by requiring accessauthentication, or the like. The policy automation engine 4032 mayinclude cognitive features, such as varying the application of policies,the configuration of policies, and the like (such as features based onstate information from the state system 4020). The policy automationengine 4032 may take feedback, as from the learning feedback system4012, such as based on one or more analytic results from the analyticsystem 4018, such as based on overall system results (such as the extentof security breaches, policy violations, and the like), local results,and analytic results. By variation and selection based on such feedback,the policy automation engine 4032 can, over time, learn to automaticallycreate, deploy, configure, and manage policies across very large numbersof devices, such as managing policies for configuration of connectionsamong IoT devices.

Methods and systems are disclosed herein for on-device sensor fusion anddata storage for industrial IoT devices, including on-device sensorfusion and data storage for an industrial IoT device, where data frommultiple sensors is multiplexed at the device for storage of a fuseddata stream. For example, pressure and temperature data may bemultiplexed into a data stream that combines pressure and temperature ina time series, such as in a byte-like structure (where time, pressure,and temperature are bytes in a data structure, so that pressure andtemperature remain linked in time, without requiring separate processingof the streams by outside systems), or by adding, dividing, multiplying,subtracting, or the like, such that the fused data can be stored on thedevice. Any of the sensor data types described throughout thisdisclosure can be fused in this manner and stored in a local data pool,in storage, or on an IoT device, such as a data collector, a componentof a machine, or the like.

In embodiments, a platform is provided having on-device sensor fusionand data storage for industrial IoT devices. In embodiments, a cognitivesystem is used for a self-organizing storage system 4028 for the datacollection system 102. Sensor data, and in particular analog sensordata, can consume large amounts of storage capacity, in particular wherea data collector 102 has multiple sensor inputs onboard or from thelocal environment. Simply storing all the data indefinitely is nottypically a favorable option, and even transmitting all of the data maystrain bandwidth limitations, exceed bandwidth permissions (such asexceeding cellular data plan capacity), or the like. Accordingly,storage strategies are needed. These typically include capturing onlyportions of the data (such as snapshots), storing data for limited timeperiods, storing portions of the data (such as intermediate orabstracted forms), and the like. With many possible selections amongthese and other options, determining the correct storage strategy may behighly complex. In embodiments, the self-organizing storage system 4028may use a cognitive system, based on learning feedback 4012, and usevarious metrics from the analytic system 4018 or other system of thehost cognitive input selection system 4114, such as overall systemmetrics, analytic metrics, and local performance indicators. Theself-organizing storage system 4028 may automatically vary storageparameters, such as storage locations (including local storage on thedata collection system 102, storage on nearby data collection systems102 (such as using peer-to-peer organization) and remote storage, suchas network-based storage), storage amounts, storage duration, type ofdata stored (including individual sensors or input sources 116, as wellas various combined or multiplexed data, such as selected under thecognitive input selection systems 4004, 4014), storage type (such asusing RAM, Flash, or other short-term memory versus available hard drivespace), storage organization (such as in raw form, in hierarchies, andthe like), and others. Variation of the parameters may be undertakenwith feedback, so that over time the data collection system 102 adaptsits storage of data to optimize itself to the conditions of itsenvironment, such as a particular industrial environment, in a way thatresults in its storing the data that is needed in the right amounts andof the right type for availability to users.

In embodiments, the local cognitive input selection system 4004 mayorganize fusion of data for various onboard sensors, external sensors(such as in the local environment) and other input sources 116 to thelocal collection system 102 into one or more fused data streams, such asusing the multiplexer 4002 to create various signals that representcombinations, permutations, mixes, layers, abstractions, data-metadatacombinations, and the like of the source analog and/or digital data thatis handled by the data collection system 102. The selection of aparticular fusion of sensors may be determined locally by the cognitiveinput selection system 4004, such as based on learning feedback from thelearning feedback system 4012, such as various overall system, analyticsystem and local system results and metrics. In embodiments, the systemmay learn to fuse particular combinations and permutations of sensors,such as in order to best achieve correct anticipation of state, asindicated by feedback of the analytic system 4018 regarding its abilityto predict future states, such as the various states handled by thestate system 4020. For example, the input selection system 4004 mayindicate selection of a sub-set of sensors among a larger set ofavailable sensors, and the inputs from the selected sensors may becombined, such as by placing input from each of them into a byte of adefined, multi-bit data structure (such as a combination by taking asignal from each at a given sampling rate or time and placing the resultinto the byte structure, then collecting and processing the bytes overtime), by multiplexing in the multiplexer 4002, such as a combination byadditive mixing of continuous signals, and the like. Any of a wide rangeof signal processing and data processing techniques for combination andfusing may be used, including convolutional techniques, coerciontechniques, transformation techniques, and the like. The particularfusion in question may be adapted to a given situation by cognitivelearning, such as by having the cognitive input selection system 4004learn, based on feedback 4012 from results (such as feedback conveyed bythe analytic system 4018), such that the local data collection system102 executes context-adaptive sensor fusion.

In embodiments, the analytic system 4018 may apply to any of a widerange of analytic techniques, including statistical and econometrictechniques (such as linear regression analysis, use similarity matrices,heat map based techniques, and the like), reasoning techniques (such asBayesian reasoning, rule-based reasoning, inductive reasoning, and thelike), iterative techniques (such as feedback, recursion, feed-forwardand other techniques), signal processing techniques (such as Fourier andother transforms), pattern recognition techniques (such as Kalman andother filtering techniques), search techniques, probabilistic techniques(such as random walks, random forest algorithms, and the like),simulation techniques (such as random walks, random forest algorithms,linear optimization and the like), and others. This may includecomputation of various statistics or measures. In embodiments, theanalytic system 4018 may be disposed, at least in part, on a datacollection system 102, such that a local analytic system can calculateone or more measures, such as measures relating to any of the itemsnoted throughout this disclosure. For example, measures of efficiency,power utilization, storage utilization, redundancy, entropy, and otherfactors may be calculated onboard, so that the data collection 102 canenable various cognitive and learning functions noted throughout thisdisclosure without dependence on a remote (e.g., cloud-based) analyticsystem.

In embodiments, the host processing system 112, a data collection system102, or both, may include, connect to, or integrate with, aself-organizing networking system 4020, which may comprise a cognitivesystem for providing machine-based, intelligent or organization ofnetwork utilization for transport of data in a data collection system,such as for handling analog and other sensor data, or other source data,such as among one or more local data collection systems 102 and a hostsystem 112. This may include organizing network utilization for sourcedata delivered to data collection systems, for feedback data, such asanalytic data provided to or via a learning feedback system 4012, datafor supporting a marketplace (such as described in connection with otherembodiments), and output data provided via output interfaces and ports4010 from one or more data collection systems 102.

Methods and systems are disclosed herein for a self-organizing datamarketplace for industrial IoT data, including where available dataelements are organized in the marketplace for consumption by consumersbased on training a self-organizing facility with a training set andfeedback from measures of marketplace success. A marketplace may be setup initially to make available data collected from one or moreindustrial environments, such as presenting data by type, by source, byenvironment, by machine, by one or more patterns, or the like (such asin a menu or hierarchy). The marketplace may vary the data collected,the organization of the data, the presentation of the data (includingpushing the data to external sites, providing links, configuring APIs bywhich the data may be accessed, and the like), the pricing of the data,or the like, such as under machine learning, which may vary differentparameters of any of the foregoing. The machine learning facility maymanage all of these parameters by self-organization, such as by varyingparameters over time (including by varying elements of the data typespresented), the data sourced used to obtain each type of data, the datastructures presented (such as byte-like structures, fused or multiplexedstructures (such as representing multiple sensor types), and statisticalstructures (such as representing various mathematical products of sensorinformation), among others), the pricing for the data, where the data ispresented, how the data is presented (such as by APIs, by links, by pushmessaging, and the like), how the data is stored, how the data isobtained, and the like. As parameters are varied, feedback may beobtained as to measures of success, such as number of views, yield(e.g., price paid) per access, total yield, per unit profit, aggregateprofit, and many others, and the self-organizing machine learningfacility may promote configurations that improve measures of success anddemote configurations that do not, so that, over time, the marketplaceis progressively configured to present favorable combinations of datatypes (e.g., those that provide robust prediction of anticipated statesof particular industrial environments of a given type), from favorablesources (e.g., those that are reliable, accurate and low priced), witheffective pricing (e.g., pricing that tends to provide high aggregateprofit from the marketplace). The marketplace may include spiders, webcrawlers, and the like to seek input data sources, such as finding datapools, connected IoT devices, and the like that publish potentiallyrelevant data. These may be trained by human users and improved bymachine learning in a manner similar to that described elsewhere in thisdisclosure.

In embodiments, a platform is provided having a self-organizing datamarketplace for industrial IoT data. Referring to FIG. 15, inembodiments, a platform is provided having a cognitive data marketplace4102, referred to in some cases as a self-organizing data marketplace,for data collected by one or more data collection systems 102 or fordata from other sensors or input sources 116 that are located in variousdata collection environments, such as industrial environments. Inaddition to data collection systems 102, this may include datacollected, handled or exchanged by IoT devices, such as cameras,monitors, embedded sensors, mobile devices, diagnostic devices andsystems, instrumentation systems, telematics systems, and the like, suchas for monitoring various parameters and features of machines, devices,components, parts, operations, functions, conditions, states, events,workflows and other elements (collectively encompassed by the term“states”) of such environments. Data may also include metadata about anyof the foregoing, such as describing data, indicating provenance,indicating elements relating to identity, access, roles, andpermissions, providing summaries or abstractions of data, or otherwiseaugmenting one or more items of data to enable further processing, suchas for extraction, transforming, loading, and processing data. Such data(such term including metadata except where context indicates otherwise)may be highly valuable to third parties, either as an individual element(such as the instance where data about the state of an environment canbe used as a condition within a process) or in the aggregate (such asthe instance where collected data, optionally over many systems anddevices in different environments can be used to develop models ofbehavior, to train learning systems, or the like). As billions of IoTdevices are deployed, with countless connections, the amount ofavailable data will proliferate. To enable access and utilization ofdata, the cognitive data marketplace 4102 enables various components,features, services, and processes for enabling users to supply, find,consume, and transact in packages of data, such as batches of data,streams of data (including event streams), data from various data pools4120, and the like. In embodiments, the cognitive data marketplace 4102may be included in, connected to, or integrated with, one or more othercomponents of a host processing architecture 4024 of a host processingsystem 112, such as a cloud-based system, as well as to various sensors,input sources 115, data collection systems 102 and the like. Thecognitive data marketplace 4102 may include marketplace interfaces 4108,which may include one or more supplier interfaces by which datasuppliers may make data available and one more consumer interfaces bywhich data may be found and acquired. The consumer interface may includean interface to a data market search system 4118, which may includefeatures that enable a user to indicate what types of data a user wishesto obtain, such as by entering keywords in a natural language searchinterface that characterize data or metadata. The search interface canuse various search and filtering techniques, including keyword matching,collaborative filtering (such as using known preferences orcharacteristics of the consumer to match to similar consumers and thepast outcomes of those other consumers), ranking techniques (such asranking based on success of past outcomes according to various metrics,such as those described in connection with other embodiments in thisdisclosure). In embodiments, a supply interface may allow an owner orsupplier of data to supply the data in one or more packages to andthrough the cognitive data marketplace 4102, such as packaging batchesof data, streams of data, or the like. The supplier may pre-packagedata, such as by providing data from a single input source 116, a singlesensor, and the like, or by providing combinations, permutations, andthe like (such as multiplexed analog data, mixed bytes of data frommultiple sources, results of extraction, loading and transformation,results of convolution, and the like), as well as by providing metadatawith respect to any of the foregoing. Packaging may include pricing,such as on a per-batch basis, on a streaming basis (such as subscriptionto an event feed or other feed or stream), on a per item basis, on arevenue share basis, or other basis. For data involving pricing, a datatransaction system 4114 may track orders, delivery, and utilization,including fulfillment of orders. The transaction system 4114 may includerich transaction features, including digital rights management, such asby managing cryptographic keys that govern access control to purchaseddata, that govern usage (such as allowing data to be used for a limitedtime, in a limited domain, by a limited set of users or roles, or for alimited purpose). The transaction system 4114 may manage payments, suchas by processing credit cards, wire transfers, debits, and other formsof consideration.

In embodiments, a cognitive data packaging system 4012 of themarketplace 4102 may use machine-based intelligence to package data,such as by automatically configuring packages of data in batches,streams, pools, or the like. In embodiments, packaging may be accordingto one or more rules, models, or parameters, such as by packaging oraggregating data that is likely to supplement or complement an existingmodel. For example, operating data from a group of similar machines(such as one or more industrial machines noted throughout thisdisclosure) may be aggregated together, such as based on metadataindicating the type of data or by recognizing features orcharacteristics in the data stream that indicate the nature of the data.In embodiments, packaging may occur using machine learning and cognitivecapabilities, such as by learning what combinations, permutations,mixes, layers, and the like of input sources 116, sensors, informationfrom data pools 4120 and information from data collection systems 102are likely to satisfy user requirements or result in measures ofsuccess. Learning may be based on learning feedback 4012, such aslearning based on measures determined in an analytic system 4018, suchas system performance measures, data collection measures, analyticmeasures, and the like. In embodiments, success measures may becorrelated to marketplace success measures, such as viewing of packages,engagement with packages, purchase or licensing of packages, paymentsmade for packages, and the like. Such measures may be calculated in ananalytic system 4018, including associating particular feedback measureswith search terms and other inputs, so that the cognitive packagingsystem 4110 can find and configure packages that are designed to provideincreased value to consumers and increased returns for data suppliers.In embodiments, the cognitive data packaging system 4110 canautomatically vary packaging, such as using different combinations,permutations, mixes, and the like, and varying weights applied to giveninput sources, sensors, data pools and the like, using learning feedback4012 to promote favorable packages and de-emphasize less favorablepackages. This may occur using genetic programming and similartechniques that compare outcomes for different packages. Feedback mayinclude state information from the state system 4020 (such as aboutvarious operating states, and the like), as well as about marketplaceconditions and states, such as pricing and availability information forother data sources. Thus, an adaptive cognitive data packaging system4110 is provided that automatically adapts to conditions to providefavorable packages of data for the marketplace 4102.

In embodiments, a cognitive data pricing system 4112 may be provided toset pricing for data packages. In embodiments, the data pricing system4112 may use a set of rules, models, or the like, such as settingpricing based on supply conditions, demand conditions, pricing ofvarious available sources, and the like. For example, pricing for apackage may be configured to be set based on the sum of the prices ofconstituent elements (such as input sources, sensor data, or the like),or to be set based on a rule-based discount to the sum of prices forconstituent elements, or the like. Rules and conditional logic may beapplied, such as rules that factor in cost factors (such as bandwidthand network usage, peak demand factors, scarcity factors, and the like),rules that factor in utilization parameters (such as the purpose,domain, user, role, duration, or the like for a package) and manyothers. In embodiments, the cognitive data pricing system 4112 mayinclude fully cognitive, intelligent features, such as using geneticprogramming including automatically varying pricing and trackingfeedback on outcomes. Outcomes on which tracking feedback may be basedinclude various financial yield metrics, utilization metrics and thelike that may be provided by calculating metrics in an analytic system4018 on data from the data transaction system 4114.

Methods and systems are disclosed herein for self-organizing data poolswhich may include self-organization of data pools based on utilizationand/or yield metrics, including utilization and/or yield metrics thatare tracked for a plurality of data pools. The data pools may initiallycomprise unstructured or loosely structured pools of data that containdata from industrial environments, such as sensor data from or aboutindustrial machines or components. For example, a data pool might takestreams of data from various machines or components in an environment,such as turbines, compressors, batteries, reactors, engines, motors,vehicles, pumps, rotors, axles, bearings, valves, and many others, withthe data streams containing analog and/or digital sensor data (of a widerange of types), data published about operating conditions, diagnosticand fault data, identifying data for machines or components, assettracking data, and many other types of data. Each stream may have anidentifier in the pool, such as indicating its source, and optionallyits type. The data pool may be accessed by external systems, such asthrough one or more interfaces or APIs (e.g., RESTful APIs), or by dataintegration elements (such as gateways, brokers, bridges, connectors, orthe like), and the data pool may use similar capabilities to get accessto available data streams. A data pool may be managed by aself-organizing machine learning facility, which may configure the datapool, such as by managing what sources are used for the pool, managingwhat streams are available, and managing APIs or other connections intoand out of the data pool. The self-organization may take feedback suchas based on measures of success that may include measures of utilizationand yield. The measures of utilization and yield that may include mayaccount for the cost of acquiring and/or storing data, as well as thebenefits of the pool, measured either by profit or by other measuresthat may include user indications of usefulness, and the like. Forexample, a self-organizing data pool might recognize that chemical andradiation data for an energy production environment are regularlyaccessed and extracted, while vibration and temperature data have notbeen used, in which case the data pool might automatically reorganize,such as by ceasing storage of vibration and/or temperature data, or byobtaining better sources of such data. This automated reorganization canalso apply to data structures, such as promoting different data types,different data sources, different data structures, and the like, throughprogressive iteration and feedback.

In embodiments, a platform is provided having self-organization of datapools based on utilization and/or yield metrics. In embodiments, thedata pools 4020 may be self-organizing data pools 4020, such as beingorganized by cognitive capabilities as described throughout thisdisclosure. The data pools 4020 may self-organize in response tolearning feedback 4012, such as based on feedback of measures andresults, including calculated in an analytic system 4018. Organizationmay include determining what data or packages of data to store in a pool(such as representing particular combinations, permutations,aggregations, and the like), the structure of such data (such as inflat, hierarchical, linked, or other structures), the duration ofstorage, the nature of storage media (such as hard disks, flash memory,SSDs, network-based storage, or the like), the arrangement of storagebits, and other parameters. The content and nature of storage may bevaried, such that a data pool 4020 may learn and adapt, such as based onstates of the host system 112, one or more data collection systems 102,storage environment parameters (such as capacity, cost, and performancefactors), data collection environment parameters, marketplaceparameters, and many others. In embodiments, pools 4020 may learn andadapt, such as by variation of the above and other parameters inresponse to yield metrics (such as return on investment, optimization ofpower utilization, optimization of revenue, and the like).

Methods and systems are disclosed herein for training AI models based onindustry-specific feedback, including training an AI model based onindustry-specific feedback that reflects a measure of utilization,yield, or impact, and where the AI model operates on sensor data from anindustrial environment. As noted above, these models may includeoperating models for industrial environments, machines, workflows,models for anticipating states, models for predicting fault andoptimizing maintenance, models for self-organizing storage (on devices,in data pools and/or in the cloud), models for optimizing data transport(such as for optimizing network coding, network-condition-sensitiverouting, and the like), models for optimizing data marketplaces, andmany others.

In embodiments, a platform is provided having training AI models basedon industry-specific feedback. In embodiments, the various embodimentsof cognitive systems disclosed herein may take inputs and feedback fromindustry-specific and domain-specific sources 116 (such as relating tooptimization of specific machines, devices, components, processes, andthe like). Thus, learning and adaptation of storage organization,network usage, combination of sensor and input data, data pooling, datapackaging, data pricing, and other features (such as for a marketplace4102 or for other purposes of the host processing system 112) may beconfigured by learning on the domain-specific feedback measures of agiven environment or application, such as an application involving IoTdevices (such as an industrial environment). This may includeoptimization of efficiency (such as in electrical, electromechanical,magnetic, physical, thermodynamic, chemical and other processes andsystems), optimization of outputs (such as for production of energy,materials, products, services and other outputs), prediction, avoidanceand mitigation of faults (such as in the aforementioned systems andprocesses), optimization of performance measures (such as returns oninvestment, yields, profits, margins, revenues and the like), reductionof costs (including labor costs, bandwidth costs, data costs, materialinput costs, licensing costs, and many others), optimization of benefits(such as relating to safety, satisfaction, health), optimization of workflows (such as optimizing time and resource allocation to processes),and others.

Methods and systems are disclosed herein for a self-organized swarm ofindustrial data collectors, including a self-organizing swarm ofindustrial data collectors that organize among themselves to optimizedata collection based on the capabilities and conditions of the membersof the swarm. Each member of the swarm may be configured withintelligence, and the ability to coordinate with other members. Forexample, a member of the swarm may track information about what dataother members are handling, so that data collection activities, datastorage, data processing, and data publishing can be allocatedintelligently across the swarm, taking into account conditions of theenvironment, capabilities of the members of the swarm, operatingparameters, rules (such as from a rules engine that governs theoperation of the swarm), and current conditions of the members. Forexample, among four collectors, one that has relatively low currentpower levels (such as a low battery), might be temporarily allocated therole of publishing data, because it may receive a dose of power from areader or interrogation device (such as an RFID reader) when it needs topublish the data. A second collector with good power levels and robustprocessing capability might be assigned more complex functions, such asprocessing data, fusing data, organizing the rest of the swarm(including self-organization under machine learning, such that the swarmis optimized over time, including by adjusting operating parameters,rules, and the like based on feedback), and the like. A third collectorin the swarm with robust storage capabilities might be assigned the taskof collecting and storing a category of data, such as vibration sensordata, that consumes considerable bandwidth. A fourth collector in theswarm, such as one with lower storage capabilities, might be assignedthe role of collecting data that can usually be discarded, such as dataon current diagnostic conditions, where only data on faults needs to bemaintained and passed along. Members of a swarm may connect bypeer-to-peer relationships by using a member as a “master” or “hub,” orby having them connect in a series or ring, where each member passesalong data (including commands) to the next, and is aware of the natureof the capabilities and commands that are suitable for the precedingand/or next member. The swarm may be used for allocation of storageacross it (such as using memory of each memory as an aggregate datastore. In these examples, the aggregate data store may support adistributed ledger, which may store transaction data, such as fortransactions involving data collected by the swarm, transactionsoccurring in the industrial environment, or the like. In embodiments,the transaction data may also include data used to manage the swarm, theenvironment, or a machine or components thereof. The swarm mayself-organize, either by machine learning capability disposed on one ormore members of the swarm, or based on instructions from an externalmachine learning facility, which may optimize storage, data collection,data processing, data presentation, data transport, and other functionsbased on managing parameters that are relevant to each. The machinelearning facility may start with an initial configuration and varyparameters of the swarm relevant to any of the foregoing (also includingvarying the membership of the swarm), such as iterating based onfeedback to the machine learning facility regarding measures of success(such as utilization measures, efficiency measures, measures of successin prediction or anticipation of states, productivity measures, yieldmeasures, profit measures, and others). Over time, the swarm may beoptimized to a favorable configuration to achieve the desired measure ofsuccess for an owner, operator, or host of an industrial environment ora machine, component, or process thereof.

The swarm 4202 may be organized based on a hierarchical organization(such as where a master data collector 102 organizes and directsactivities of one or more subservient data collectors 102), acollaborative organization (such as where decision-making for theorganization of the swarm 4202 is distributed among the data collectors102 (such as using various models for decision-making, such as votingsystems, points systems, least-cost routing systems, prioritizationsystems, and the like), and the like.) In embodiments, one or more ofthe data collectors 102 may have mobility capabilities, such as in caseswhere a data collector is disposed on or in a mobile robot, drone,mobile submersible, or the like, so that organization may include thelocation and positioning of the data collectors 102. Data collectionsystems 102 may communicate with each other and with the host processingsystem 112, including sharing an aggregate allocated storage spaceinvolving storage on or accessible to one or more of the collectors(which in embodiment may be treated as a unified storage space even ifphysically distributed, such as using virtualization capabilities).Organization may be automated based on one or more rules, models,conditions, processes, or the like (such as embodied or executed byconditional logic), and organization may be governed by policies, suchas handled by the policy engine. Rules may be based on industry,application- and domain-specific objects, classes, events, workflows,processes, and systems, such as by setting up the swarm 4202 to collectselected types of data at designated places and times, such ascoordinated with the foregoing. For example, the swarm 4202 may assigndata collectors 102 to serially collect diagnostic, sensor,instrumentation and/or telematic data from each of a series of machinesthat execute an industrial process (such as a robotic manufacturingprocess), such as at the time and location of the input to and outputfrom each of those machines. In embodiments, self-organization may becognitive, such as where the swarm varies one or more collectionparameters and adapts the selection of parameters, weights applied tothe parameters, or the like, over time. In examples, this may be inresponse to learning and feedback, such as from the learning feedbacksystem 4012 that may be based on various feedback measures that may bedetermined by applying the analytic system 4018 (which in embodimentsmay reside on the swarm 4202, the host processing system 112, or acombination thereof) to data handled by the swarm 4202 or to otherelements of the various embodiments disclosed herein (includingmarketplace elements and others). Thus, the swarm 4202 may displayadaptive behavior, such as adapting to the current state 4020 or ananticipated state of its environment (accounting for marketplacebehavior), behavior of various objects (such as IoT devices, machines,components, and systems), processes (including events, states,workflows, and the like), and other factors at a given time. Parametersthat may be varied in a process of variation (such as in a neural net,self-organizing map, or the like), selection, promotion, or the like(such as those enabled by genetic programming or other AI-basedtechniques). Parameters that may be managed, varied, selected andadapted by cognitive, machine learning may include storage parameters(location, type, duration, amount, structure and the like across theswarm 4202), network parameters (such as how the swarm 4202 isorganized, such as in mesh, peer-to-peer, ring, serial, hierarchical andother network configurations as well as bandwidth utilization, datarouting, network protocol selection, network coding type, and othernetworking parameters), security parameters (such as settings forvarious security applications and services), location and positioningparameters (such as routing movement of mobile data collectors 102 tolocations, positioning and orienting collectors 102 and the likerelative to points of data acquisition, relative to each other, andrelative to locations where network availability may be favorable, amongothers), input selection parameters (such as input selection amongsensors, input sources 116 and the like for each collector 102 and forthe aggregate collection), data combination parameters (such as thosefor sensor fusion, input combination, multiplexing, mixing, layering,convolution, and other combinations), power parameters (such asparameters based on power levels and power availability for one or morecollectors 102 or other objects, devices, or the like), states(including anticipated states and conditions of the swarm 4202,individual collection systems 102, the host processing system 112 or oneor more objects in an environment), events, and many others. Feedbackmay be based on any of the kinds of feedback described herein, such thatover time the swarm may adapt to its current and anticipated situationto achieve a wide range of desired objectives.

Methods and systems are disclosed herein for an industrial IoTdistributed ledger, including a distributed ledger supporting thetracking of transactions executed in an automated data marketplace forindustrial IoT data. A distributed ledger may distribute storage acrossdevices, using a secure protocol, such as those used forcryptocurrencies (such as the Blockchain™ protocol used to support theBitcoin™ currency). A ledger or similar transaction record, which maycomprise a structure where each successive member of a chain stores datafor previous transactions, and a competition can be established todetermine which of alternative data stored data structures is “best”(such as being most complete), can be stored across data collectors,industrial machines or components, data pools, data marketplaces, cloudcomputing elements, servers, and/or on the IT infrastructure of anenterprise (such as an owner, operator or host of an industrialenvironment or of the systems disclosed herein). The ledger ortransaction may be optimized by machine learning, such as to providestorage efficiency, security, redundancy, or the like.

In embodiments, the cognitive data marketplace 4102 may use a securearchitecture for tracking and resolving transactions, such as adistributed ledger 4004, wherein transactions in data packages aretracked in a chained, distributed data structure, such as a Blockchain™,allowing forensic analysis and validation where individual devices storea portion of the ledger representing transactions in data packages. Thedistributed ledger 4004 may be distributed to IoT devices, to data pools4020, to data collection systems 102, and the like, so that transactioninformation can be verified without reliance on a single, centralrepository of information. The transaction system 4114 may be configuredto store data in the distributed ledger 4004 and to retrieve data fromit (and from constituent devices) in order to resolve transactions.Thus, a distributed ledger 4004 for handling transactions in data, suchas for packages of IoT data, is provided. In embodiments, theself-organizing storage system 4028 may be used for optimizing storageof distributed ledger data, as well as for organizing storage ofpackages of data, such as IoT data, that can be presented in themarketplace 4102.

Methods and systems are disclosed herein for a network-sensitivecollector, including a network condition-sensitive, self-organizing,multi-sensor data collector that can optimize based on bandwidth,quality of service, pricing and/or other network conditions. Networksensitivity can include awareness of the price of data transport (suchas allowing the system to pull or push data during off-peak periods orwithin the available parameters of paid data plans), the quality of thenetwork (such as to avoid periods where errors are likely), the qualityof environmental conditions (such as delaying transmission until signalquality is good, such as when a collector emerges from a shieldedenvironment, avoiding wasting use of power when seeking a signal whenshielded, such as by large metal structures typically of industrialenvironments), and the like.

Methods and systems are disclosed herein for a remotely organizeduniversal data collector that can power up and down sensor interfacesbased on need and/or conditions identified in an industrial datacollection environment. For example, interfaces can recognize whatsensors are available and interfaces and/or processors can be turned onto take input from such sensors, including hardware interfaces thatallow the sensors to plug in to the data collector, wireless datainterfaces (such as where the collector can ping the sensor, optionallyproviding some power via an interrogation signal), and softwareinterfaces (such as for handling particular types of data). Thus, acollector that is capable of handling various kinds of data can beconfigured to adapt to the particular use in a given environment. Inembodiments, configuration may be automatic or under machine learning,which may improve configuration by optimizing parameters based onfeedback measures over time.

Methods and systems are disclosed herein for self-organizing storage fora multi-sensor data collector, including self-organizing storage for amulti-sensor data collector for industrial sensor data. Self-organizingstorage may allocate storage based on application of machine learning,which may improve storage configuration based on feedback measure overtime. Storage may be optimized by configuring what data types are used(e.g., byte-like structures, structures representing fused data frommultiple sensors, structures representing statistics or measurescalculated by applying mathematical functions on data, and the like), byconfiguring compression, by configuring data storage duration, byconfiguring write strategies (such as by striping data across multiplestorage devices, using protocols where one device stores instructionsfor other devices in a chain, and the like), and by configuring storagehierarchies, such as by providing pre-calculated intermediate statisticsto facilitate more rapid access to frequently accessed data items. Thus,highly intelligent storage systems may be configured and optimized,based on feedback, over time.

Methods and systems are disclosed herein for self-organizing networkcoding for a multi-sensor data network, including self-organizingnetwork coding for a data network that transports data from multiplesensors in an industrial data collection environment. Network coding,including random linear network coding, can enable highly efficient andreliable transport of large amounts of data over various kinds ofnetworks. Different network coding configurations can be selected, basedon machine learning, to optimize network coding and other networktransport characteristics based on network conditions, environmentalconditions, and other factors, such as the nature of the data beingtransported, environmental conditions, operating conditions, and thelike (including by training a network coding selection model over timebased on feedback of measures of success, such as any of the measuresdescribed herein).

In embodiments, a platform is provided having a self-organizing networkcoding for multi-sensor data network. A cognitive system may vary one ormore parameters for networking, such as network type selection (e.g.,selecting among available local, cellular, satellite, Wi-Fi, Bluetooth™,NFC, Zigbee® and other networks), network selection (such as selecting aspecific network, such as one that is known to have desired securityfeatures), network coding selection (such as selecting a type of networkcoding for efficient transport[such as random linear network coding,fixed coding, and others]), network timing selection (such asconfiguring delivery based on network pricing conditions, traffic andthe like), network feature selection (such as selecting cognitivefeatures, security features, and the like), network conditions (such asnetwork quality based on current environmental or operation conditions),network feature selection (such as enabling available authentication,permission and similar systems), network protocol selection (such asamong HTTP, IP, TCP/IP, cellular, satellite, serial, packet, streaming,and many other protocols), and others. Given bandwidth constraints,price variations, sensitivity to environmental factors, securityconcerns, and the like, selecting the optimal network configuration canbe highly complex and situation dependent. The self-organizingnetworking system 4030 may vary combinations and permutations of theseparameters while taking input from a learning feedback system 4012 suchas using information from the analytic system 4018 about variousmeasures of outcomes. In the many examples, outcomes may include overallsystem measures, analytic success measures, and local performanceindicators. In embodiments, input from a learning feedback system 4012may include information from various sensors and input sources 116,information from the state system 4020 about states (such as events,environmental conditions, operating conditions, and many others, orother information) or taking other inputs. By variation and selection ofalternative configurations of networking parameters in different states,the self-organizing networking system may find configurations that arewell-adapted to the environment that is being monitored or controlled bythe host system 112, such as the instance where one or more datacollection systems 102 are located and that are well-adapted to emergingnetwork conditions. Thus, a self-organizing, network-condition-adaptivedata collection system is provided.

Referring to FIG. 42, a data collection system 102 may have one or moreoutput interfaces and/or ports 4010. These may include network ports andconnections, application programming interfaces, and the like. Methodsand systems are disclosed herein for a haptic or multi-sensory userinterface, including a wearable haptic or multi-sensory user interfacefor an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs. For example, an interface may, basedon a data structure configured to support the interface, be set up toprovide a user with input or feedback, such as based on data fromsensors in the environment. For example, if a fault condition based on avibration data (such as resulting from a bearing being worn down, anaxle being misaligned, or a resonance condition between machines) isdetected, it can be presented in a haptic interface by vibration of aninterface, such as shaking a wrist-worn device. Similarly, thermal dataindicating overheating could be presented by warming or cooling awearable device, such as while a worker is working on a machine andcannot necessarily look at a user interface. Similarly, electrical ormagnetic data may be presented by a buzzing, and the like, such as toindicate presence of an open electrical connection or wire, etc. Thatis, a multi-sensory interface can intuitively help a user (such as auser with a wearable device) get a quick indication of what is going onin an environment, with the wearable interface having various modes ofinteraction that do not require a user to have eyes on a graphical UI,which may be difficult or impossible in many industrial environmentswhere a user needs to keep an eye on the environment.

In embodiments, a platform is provided having a wearable haptic userinterface for an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs. In embodiments, a haptic userinterface 4302 is provided as an output for a data collection system102, such as a system for handling and providing information forvibration, heat, electrical, and/or sound outputs, such as to one ormore components of the data collection system 102 or to another system,such as a wearable device, mobile phone, or the like. A data collectionsystem 102 may be provided in a form factor suitable for deliveringhaptic input to a user, such as vibration, warming or cooling, buzzing,or the like, such as input disposed in headgear, an armband, a wristbandor watch, a belt, an item of clothing, a uniform, or the like. In suchcases, data collection systems 102 may be integrated with gear,uniforms, equipment, or the like worn by users, such as individualsresponsible for operating or monitoring an industrial environment. Inembodiments, signals from various sensors or input sources (or selectivecombinations, permutations, mixes, and the like, as managed by one ormore of the cognitive input selection systems 4004, 4014) may triggerhaptic feedback. For example, if a nearby industrial machine isoverheating, the haptic interface may alert a user by warming up, or bysending a signal to another device (such as a mobile phone) to warm up.If a system is experiencing unusual vibrations, the haptic interface mayvibrate. Thus, through various forms of haptic input, a data collectionsystem 102 may inform users of the need to attend to one or moredevices, machines, or other factors (such as those in an industrialenvironment) without requiring them to read messages or divert theirvisual attention away from the task at hand. The haptic interface, andselection of what outputs should be provided, may be considered in thecognitive input selection systems 4004, 4014. For example, user behavior(such as responses to inputs) may be monitored and analyzed in ananalytic system 4018, and feedback may be provided through the learningfeedback system 4012, so that signals may be provided based on the rightcollection or package of sensors and inputs, at the right time and inthe right manner, to optimize the effectiveness of the haptic system4202. This may include rule-based or model-based feedback (such asproviding outputs that correspond in some logical fashion to the sourcedata that is being conveyed). In embodiments, a cognitive haptic systemmay be provided, where selection of inputs or triggers for hapticfeedback, selection of outputs, timing, intensity levels, durations, andother parameters (or weights applied to them) may be varied in a processof variation, promotion, and selection (such as using geneticprogramming) with feedback based on real world responses to feedback inactual situations or based on results of simulation and testing of userbehavior. Thus, an adaptive haptic interface for a data collectionsystem 102 is provided, which may learn and adapt feedback to satisfyrequirements and to optimize the impact on user behavior, such as foroverall system outcomes, data collection outcomes, analytic outcomes,and the like.

Methods and systems are disclosed herein for a presentation layer forAR/VR industrial glasses, where heat map elements are presented based onpatterns and/or parameters in collected data. Methods and systems aredisclosed herein for condition-sensitive, self-organized tuning of AR/VRinterfaces based on feedback metrics and/or training in industrialenvironments. In embodiments, any of the data, measures, and the likedescribed throughout this disclosure can be presented by visualelements, overlays, and the like for presentation in the AR/VRinterfaces, such as in industrial glasses, on AR/VR interfaces on smartphones or tablets, on AR/VR interfaces on data collectors (which may beembodied in smart phones or tablets), on displays located on machines orcomponents, and/or on displays located in industrial environments.

In embodiments, a platform is provided having heat maps displayingcollected data for AR/VR. In embodiments, a platform is provided havingheat maps 4204 displaying collected data from a data collection system102 for providing input to an AR/VR interface 4208. In embodiments, theheat map interface 4304 is provided as an output for a data collectionsystem 102, such as for handling and providing information forvisualization of various sensor data and other data (such as map data,analog sensor data, and other data), such as to one or more componentsof the data collection system 102 or to another system, such as a mobiledevice, tablet, dashboard, computer, AR/VR device, or the like. A datacollection system 102 may be provided in a form factor suitable fordelivering visual input to a user, such as the presentation of a mapthat includes indicators of levels of analog and digital sensor data(such as data indicating levels of rotation, vibration, heating orcooling, pressure, and many other conditions). In such cases, datacollection systems 102 may be integrated with equipment, or the likethat are used by individuals responsible for operating or monitoring anindustrial environment. In embodiments, signals from various sensors orinput sources (or selective combinations, permutations, mixes, and thelike, as managed by one or more of the cognitive input selection systems4004, 4014) may provide input data to a heat map. Coordinates mayinclude real world location coordinates (such as geo-location orlocation on a map of an environment), as well as other coordinates, suchas time-based coordinates, frequency-based coordinates, or othercoordinates that allow for representation of analog sensor signals,digital signals, input source information, and various combinations, ina map-based visualization, such that colors may represent varying levelsof input along the relevant dimensions. For example, if a nearbyindustrial machine is overheating, the heat map interface may alert auser by showing a machine in bright red. If a system is experiencingunusual vibrations, the heat map interface may show a different colorfor a visual element for the machine, or it may cause an icon or displayelement representing the machine to vibrate in the interface, callingattention to the element. Clicking, touching, or otherwise interactingwith the map can allow a user to drill down and see underlying sensor orinput data that is used as an input to the heat map display. Thus,through various forms of display, a data collection system 102 mayinform users of the need to attend to one or more devices, machines, orother factors, such as those in an industrial environment, withoutrequiring them to read text-based messages or input. The heat mapinterface, and selection of what outputs should be provided, may beconsidered in the cognitive input selection systems 4004, 4014. Forexample, user behavior (such as responses to inputs or displays) may bemonitored and analyzed in an analytic system 4018, and feedback may beprovided through the learning feedback system 4012, so that signals maybe provided based on the right collection or package of sensors andinputs, at the right time and in the right manner, to optimize theeffectiveness of the heat map UI 4304. This may include rule-based ormodel-based feedback (such as feedback providing outputs that correspondin some logical fashion to the source data that is being conveyed). Inembodiments, a cognitive heat map system may be provided, whereselection of inputs or triggers for heat map displays, selection ofoutputs, colors, visual representation elements, timing, intensitylevels, durations and other parameters (or weights applied to them) maybe varied in a process of variation, promotion and selection (such asselection using genetic programming) with feedback based on real worldresponses to feedback in actual situations or based on results ofsimulation and testing of user behavior. Thus, an adaptive heat mapinterface for a data collection system 102, or data collected thereby102, or data handled by a host processing system 112, is provided, whichmay learn and adapt feedback to satisfy requirements and to optimize theimpact on user behavior and reaction, such as for overall systemoutcomes, data collection outcomes, analytic outcomes, and the like.

In embodiments, a platform is provided having automatically tuned AR/VRvisualization of data collected by a data collector. In embodiments, aplatform is provided having an automatically tuned AR/VR visualizationsystem 4308 for visualization of data collected by a data collectionsystem 102, such as the case where the data collection system 102 has anAR/VR interface 4208 or provides input to an AR/VR interface 4308 (suchas a mobile phone positioned in a virtual reality or AR headset, a setof AR glasses, or the like). In embodiments, the AR/VR system 4308 isprovided as an output interface of a data collection system 102, such asa system for handling and providing information for visualization ofvarious sensor data and other data (such as map data, analog sensordata, and other data), such as to one or more components of the datacollection system 102 or to another system, such as a mobile device,tablet, dashboard, computer, AR/VR device, or the like. A datacollection system 102 may be provided in a form factor suitable fordelivering AR or VR visual, auditory, or other sensory input to a user,such as by presenting one or more displays such as 3D-realisticvisualizations, objects, maps, camera overlays, or other overlayelements, maps and the like that include or correspond to indicators oflevels of analog and digital sensor data (such as data indicating levelsof rotation, vibration, heating or cooling, pressure and many otherconditions, to input sources 116, or the like). In such cases, datacollection systems 102 may be integrated with equipment, or the likethat are used by individuals responsible for operating or monitoring anindustrial environment.

In embodiments, signals from various sensors or input sources (orselective combinations, permutations, mixes, and the like as managed byone or more of the cognitive input selection systems 4004, 4014) mayprovide input data to populate, configure, modify, or otherwisedetermine the AR/VR element. Visual elements may include a wide range oficons, map elements, menu elements, sliders, toggles, colors, shapes,sizes, and the like, for representation of analog sensor signals,digital signals, input source information, and various combinations. Inmany examples, colors, shapes, and sizes of visual overlay elements mayrepresent varying levels of input along the relevant dimensions for asensor or combination of sensors. In further examples, if a nearbyindustrial machine is overheating, an AR element may alert a user byshowing an icon representing that type of machine in flashing red colorin a portion of the display of a pair of AR glasses. If a system isexperiencing unusual vibrations, a virtual reality interface showingvisualization of the components of the machine (such as an overlay of acamera view of the machine with 3D visualization elements) may show avibrating component in a highlighted color, with motion, or the like, toensure the component stands out in a virtual reality environment beingused to help a user monitor or service the machine. Clicking, touching,moving eyes toward, or otherwise interacting with a visual element in anAR/VR interface may allow a user to drilldown and see underlying sensoror input data that is used as an input to the display. Thus, throughvarious forms of display, a data collection system 102 may inform usersof the need to attend to one or more devices, machines, or other factors(such as in an industrial environment), without requiring them to readtext-based messages or input or divert attention from the applicableenvironment (whether it is a real environment with AR features or avirtual environment, such as for simulation, training, or the like).

The AR/VR output interface 4208, and selection and configuration of whatoutputs or displays should be provided, may be handled in the cognitiveinput selection systems 4004, 4014. For example, user behavior (such asresponses to inputs or displays) may be monitored and analyzed in ananalytic system 4018, and feedback may be provided through the learningfeedback system 4012, so that AR/VR display signals may be providedbased on the right collection or package of sensors and inputs, at theright time and in the right manner, to optimize the effectiveness of theAR/VR UI 4308. This may include rule-based or model-based feedback (suchas providing outputs that correspond in some logical fashion to thesource data that is being conveyed). In embodiments, a cognitively tunedAR/VR interface control system 4308 may be provided, where selection ofinputs or triggers for AR/VR display elements, selection of outputs(such as colors, visual representation elements, timing, intensitylevels, durations and other parameters [or weights applied to them]) andother parameters of a VR/AR environment may be varied in a process ofvariation, promotion and selection (such as the use of geneticprogramming) with feedback based on real world responses in actualsituations or based on results of simulation and testing of userbehavior. Thus, an adaptive, tuned AR/VR interface for a data collectionsystem 102, or data collected thereby 102, or data handled by a hostprocessing system 112, is provided, which may learn and adapt feedbackto satisfy requirements and to optimize the impact on user behavior andreaction, such as for overall system outcomes, data collection outcomes,analytic outcomes, and the like.

As noted above, methods and systems are disclosed herein for continuousultrasonic monitoring, including providing continuous ultrasonicmonitoring of rotating elements and bearings of an energy productionfacility. Embodiments include using continuous ultrasonic monitoring ofan industrial environment as a source for a cloud-deployed patternrecognizer. Embodiments include using continuous ultrasonic monitoringto provide updated state information to a state machine that is used asan input to a cloud-deployed pattern recognizer. Embodiments includemaking available continuous ultrasonic monitoring information to a userbased on a policy declared in a policy engine. Embodiments includestoring continuous ultrasonic monitoring data with other data in a fuseddata structure on an industrial sensor device. Embodiments includemaking a stream of continuous ultrasonic monitoring data from anindustrial environment available as a service from a data marketplace.Embodiments include feeding a stream of continuous ultrasonic monitoringdata into a self-organizing data pool. Embodiments include training amachine learning model to monitor a continuous ultrasonic monitoringdata stream where the model is based on a training set created fromhuman analysis of such a data stream, and is improved based on datacollected on performance in an industrial environment.

Embodiments include a swarm of data collectors that include at least onedata collector for continuous ultrasonic monitoring of an industrialenvironment and at least one other type of data collector. Embodimentsinclude using a distributed ledger to store time-series data fromcontinuous ultrasonic monitoring across multiple devices. Embodimentsinclude collecting a stream of continuous ultrasonic data in aself-organizing data collector, a network-sensitive data collector, aremotely organized data collector, a data collector havingself-organized storage and the like. Embodiments include usingself-organizing network coding to transport a stream of ultrasonic datacollected from an industrial environment. Embodiments include conveyingan indicator of a parameter of a continuously collected ultrasonic datastream via an interface where the interface is one of a sensoryinterface of a wearable device, a heat map visual interface of awearable device, an interface that operates with self-organized tuningof the interface layer, and the like.

As noted above, methods and systems are disclosed herein forcloud-based, machine pattern recognition based on fusion of remoteanalog industrial sensors. Embodiments include taking input from aplurality of analog sensors disposed in an industrial environment,multiplexing the sensors into a multiplexed data stream, feeding thedata stream into a cloud-deployed machine learning facility, andtraining a model of the machine learning facility to recognize a definedpattern associated with the industrial environment. Embodiments includeusing a cloud-based pattern recognizer on input states from a statemachine that characterizes states of an industrial environment.Embodiments include deploying policies by a policy engine that governwhat data can be used by what users and for what purpose in cloud-based,machine learning. Embodiments include using a cloud-based platform toidentify patterns in data across a plurality of data pools that containdata published from industrial sensors. Embodiments include training amodel to identify preferred sensor sets to diagnose a condition of anindustrial environment, where a training set is created by a human userand the model is improved based on feedback from data collected aboutconditions in an industrial environment.

Embodiments include a swarm of data collectors that is governed by apolicy that is automatically propagated through the swarm. Embodimentsinclude using a distributed ledger to store sensor fusion informationacross multiple devices. Embodiments include feeding input from a set ofdata collectors into a cloud-based pattern recognizer that uses datafrom multiple sensors for an industrial environment. The data collectorsmay be self-organizing data collectors, network-sensitive datacollectors, remotely organized data collectors, a set of data collectorshaving self-organized storage, and the like. Embodiments include asystem for data collection in an industrial environment withself-organizing network coding for data transport of data fused frommultiple sensors in the environment. Embodiments include conveyinginformation formed by fusing inputs from multiple sensors in anindustrial data collection system in an interface such as amulti-sensory interface, a heat map interface, an interface thatoperates with self-organized tuning of the interface layer, and thelike.

As noted above, methods and systems are disclosed herein forcloud-based, machine pattern analysis of state information from multipleanalog industrial sensors to provide anticipated state information foran industrial system. Embodiments include using a policy engine todetermine what state information can be used for cloud-based machineanalysis. Embodiments include feeding inputs from multiple devices thathave fused and on-device storage of multiple sensor streams into acloud-based pattern recognizer to determine an anticipated state of anindustrial environment. Embodiments include making an output, such asanticipated state information, from a cloud-based machine patternrecognizer that analyzes fused data from remote, analog industrialsensors available as a data service in a data marketplace. Embodimentsinclude using a cloud-based pattern recognizer to determine ananticipated state of an industrial environment based on data collectedfrom data pools that contain streams of information from machines in theenvironment. Embodiments include training a model to identify preferredstate information to diagnose a condition of an industrial environment,where a training set is created by a human user and the model isimproved based on feedback from data collected about conditions in anindustrial environment. Embodiments include a swarm of data collectorsthat feeds a state machine that maintains current state information foran industrial environment. Embodiments include using a distributedledger to store historical state information for fused sensor states aself-organizing data collector that feeds a state machine that maintainscurrent state information for an industrial environment. Embodimentsinclude a data collector that feeds a state machine that maintainscurrent state information for an industrial environment where the datacollector may be a network sensitive data collector, a remotelyorganized data collector, a data collector with self-organized storage,and the like. Embodiments include a system for data collection in anindustrial environment with self-organizing network coding for datatransport and maintains anticipated state information for theenvironment. Embodiments include conveying anticipated state informationdetermined by machine learning in an industrial data collection systemin an interface where the interface may be one or more of a multisensoryinterface, a heat map interface an interface that operates withself-organized tuning of the interface layer, and the like.

As noted above, methods and systems are disclosed herein for acloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices, including a cloud-based policy automationengine for IoT, enabling creation, deployment and management of policiesthat apply to IoT devices. Policies can relate to data usage to anon-device storage system that stores fused data from multiple industrialsensors, or what data can be provided to whom in a self-organizingmarketplace for IoT sensor data. Policies can govern how aself-organizing swarm or data collector should be organized for aparticular industrial environment, how a network-sensitive datacollector should use network bandwidth for a particular industrialenvironment, how a remotely organized data collector should collect, andmake available, data relating to a specified industrial environment, orhow a data collector should self-organize storage for a particularindustrial environment. Policies can be deployed across a set ofself-organizing pools of data that contain data streamed from industrialsensing devices to govern use of data from the pools or stored on adevice that governs use of storage capabilities of the device for adistributed ledger. Embodiments include training a model to determinewhat policies should be deployed in an industrial data collectionsystem. Embodiments include a system for data collection in anindustrial environment with a policy engine for deploying policy withinthe system and, optionally, self-organizing network coding for datatransport, wherein in certain embodiments, a policy applies to how datawill be presented in a multi-sensory interface, a heat map visualinterface, or in an interface that operates with self-organized tuningof the interface layer.

As noted above, methods and systems are disclosed herein for on-devicesensor fusion and data storage for industrial IoT devices, such as anindustrial data collector, including self-organizing, remotelyorganized, or network-sensitive industrial data collectors, where datafrom multiple sensors is multiplexed at the device for storage of afused data stream. Embodiments include a self-organizing marketplacethat presents fused sensor data that is extracted from on-device storageof IoT devices. Embodiments include streaming fused sensor informationfrom multiple industrial sensors and from an on-device data storagefacility to a data pool. Embodiments include training a model todetermine what data should be stored on a device in a data collectionenvironment. Embodiments include a self-organizing swarm of industrialdata collectors that organize among themselves to optimize datacollection, where at least some of the data collectors have on-devicestorage of fused data from multiple sensors. Embodiments include storingdistributed ledger information with fused sensor information on anindustrial IoT device. Embodiments include a system for data collectionwith on-device sensor fusion, such as of industrial sensor data and,optionally, self-organizing network coding for data transport, wheredata structures are stored to support alternative, multi-sensory modesof presentation, visual heat map modes of presentation, and/or aninterface that operates with self-organized tuning of the interfacelayer.

As noted above, methods and systems are disclosed herein for aself-organizing data marketplace for industrial IoT data, whereavailable data elements are organized in the marketplace for consumptionby consumers based on training a self-organizing facility with atraining set and feedback from measures of marketplace success.Embodiments include organizing a set of data pools in a self-organizingdata marketplace based on utilization metrics for the data pools.Embodiments include training a model to determine pricing for data in adata marketplace. The data marketplace is fed with data streams from aself-organizing swarm of industrial data collectors, a set of industrialdata collectors that have self-organizing storage, or self-organizing,network-sensitive, or remotely organized industrial data collectors.Embodiments include using a distributed ledger to store transactionaldata for a self-organizing marketplace for industrial IoT data.Embodiments include using self-organizing network coding for datatransport to a marketplace for sensor data collected in industrialenvironments. Embodiments include providing a library of data structuressuitable for presenting data in alternative, multi-sensory interfacemodes in a data marketplace, in heat map visualization, and/or ininterfaces that operate with self-organized tuning of the interfacelayer.

As noted above, methods and systems are disclosed herein forself-organizing data pools such as those that self-organize based onutilization and/or yield metrics that may be tracked for a plurality ofdata pools. In embodiments, the pools contain data from self-organizingdata collectors. Embodiments include training a model to present themost valuable data in a data marketplace, where training is based onindustry-specific measures of success. Embodiments include populating aset of self-organizing data pools with data from a self-organizing swarmof data collectors. Embodiments include using a distributed ledger tostore transactional information for data that is deployed in data pools,where the distributed ledger is distributed across the data pools.Embodiments include populating a set of self-organizing data pools withdata from a set of network-sensitive or remotely organized datacollectors or a set of data collectors having self-organizing storage.Embodiments include a system for data collection in an industrialenvironment with self-organizing pools for data storage andself-organizing network coding for data transport, such as a system thatincludes a source data structure for supporting data presentation in amulti-sensory interface, in a heat map interface, and/or in an interfacethat operates with self-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for training AImodels based on industry-specific feedback, such as that reflects ameasure of utilization, yield, or impact, where the AI model operates onsensor data from an industrial environment. Embodiments include traininga swarm of data collectors, or data collectors, such as remotelyorganized, self-organizing, or network-sensitive data collectors, basedon industry-specific feedback or network and industrial conditions in anindustrial environment, such as to configure storage. Embodimentsinclude training an AI model to identify and use available storagelocations in an industrial environment for storing distributed ledgerinformation. Embodiments include training a remote organizer for aremotely organized data collector based on industry-specific feedbackmeasures. Embodiments include a system for data collection in anindustrial environment with cloud-based training of a network codingmodel for organizing network coding for data transport or a facilitythat manages presentation of data in a multi-sensory interface, in aheat map interface, and/or in an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for aself-organized swarm of industrial data collectors that organize amongthemselves to optimize data collection based on the capabilities andconditions of the members of the swarm. Embodiments include deployingdistributed ledger data structures across a swarm of data. Datacollectors may be network-sensitive data collectors configured forremote organization or have self-organizing storage. Systems for datacollection in an industrial environment with a swarm can include aself-organizing network coding for data transport. Systems includeswarms that relay information for use in a multi-sensory interface, in aheat map interface, and/or in an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for anindustrial IoT distributed ledger, including a distributed ledgersupporting the tracking of transactions executed in an automated datamarketplace for industrial IoT data. Embodiments include aself-organizing data collector that is configured to distributecollected information to a distributed ledger. Embodiments include anetwork-sensitive data collector that is configured to distributecollected information to a distributed ledger based on networkconditions. Embodiments include a remotely organized data collector thatis configured to distribute collected information to a distributedledger based on intelligent, remote management of the distribution.Embodiments include a data collector with self-organizing local storagethat is configured to distribute collected information to a distributedledger. Embodiments include a system for data collection in anindustrial environment using a distributed ledger for data storage andself-organizing network coding for data transport, wherein data storageis of a data structure supporting a haptic interface for datapresentation, a heat map interface for data presentation, and/or aninterface that operates with self-organized tuning of the interfacelayer.

As noted above, methods and systems are disclosed herein for aself-organizing collector, including a self-organizing, multi-sensordata collector that can optimize data collection, power and/or yieldbased on conditions in its environment, and is optionally responsive toremote organization. Embodiments include a self-organizing datacollector that organizes at least in part based on network conditions.Embodiments include a self-organizing data collector withself-organizing storage for data collected in an industrial datacollection environment. Embodiments include a system for data collectionin an industrial environment with self-organizing data collection andself-organizing network coding for data transport. Embodiments include asystem for data collection in an industrial environment with aself-organizing data collector that feeds a data structure supporting ahaptic or multi-sensory wearable interface for data presentation, a heatmap interface for data presentation, and/or an interface that operateswith self-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for anetwork-sensitive collector, including a network condition-sensitive,self-organizing, multi-sensor data collector that can optimize based onbandwidth, quality of service, pricing, and/or other network conditions.Embodiments include a remotely organized, network condition-sensitiveuniversal data collector that can power up and down sensor interfacesbased on need and/or conditions identified in an industrial datacollection environment, including network conditions. Embodimentsinclude a network-condition sensitive data collector withself-organizing storage for data collected in an industrial datacollection environment. Embodiments include a network-conditionsensitive data collector with self-organizing network coding for datatransport in an industrial data collection environment. Embodimentsinclude a system for data collection in an industrial environment with anetwork-sensitive data collector that relays a data structure supportinga haptic wearable interface for data presentation, a heat map interfacefor data presentation, and/or an interface that operates withself-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein for a remotelyorganized universal data collector that can power up and down sensorinterfaces based on need and/or conditions identified in an industrialdata collection environment. Embodiments include a remotely organizeduniversal data collector with self-organizing storage for data collectedin an industrial data collection environment. Embodiments include asystem for data collection in an industrial environment with remotecontrol of data collection and self-organizing network coding for datatransport. Embodiments include a remotely organized data collector forstoring sensor data and delivering instructions for use of the data in ahaptic or multi-sensory wearable interface, in a heat map visualinterface, and/or in an interface that operates with self-organizedtuning of the interface layer.

As noted above, methods and systems are disclosed herein forself-organizing storage for a multi-sensor data collector, includingself-organizing storage for a multi-sensor data collector for industrialsensor data. Embodiments include a system for data collection in anindustrial environment with self-organizing data storage andself-organizing network coding for data transport. Embodiments include adata collector with self-organizing storage for storing sensor data andinstructions for translating the data for use in a haptic wearableinterface, in a heat map presentation interface, and/or in an interfacethat operates with self-organized tuning of the interface layer.

As noted above, methods and systems are disclosed herein forself-organizing network coding for a multi-sensor data network,including self-organizing network coding for a data network thattransports data from multiple sensors in an industrial data collectionenvironment. The system includes a data structure supporting a hapticwearable interface for data presentation, a heat map interface for datapresentation, and/or self-organized tuning of an interface layer fordata presentation.

As noted above, methods and systems are disclosed herein for a haptic ormulti-sensory user interface, including a wearable haptic ormulti-sensory user interface for an industrial sensor data collector,with vibration, heat, electrical, and/or sound outputs. Embodimentsinclude a wearable haptic user interface for conveying industrial stateinformation from a data collector, with vibration, heat, electrical,and/or sound outputs. The wearable also has a visual presentation layerfor presenting a heat map that indicates a parameter of the data.Embodiments include condition-sensitive, self-organized tuning of AR/VRinterfaces and multi-sensory interfaces based on feedback metrics and/ortraining in industrial environments.

As noted above, methods and systems are disclosed herein for apresentation layer for AR/VR industrial glasses, where heat map elementsare presented based on patterns and/or parameters in collected data.Embodiments include condition-sensitive, self-organized tuning of a heatmap AR/VR interface based on feedback metrics and/or training inindustrial environments. As noted above, methods and systems aredisclosed herein for condition-sensitive, self-organized tuning of AR/VRinterfaces based on feedback metrics and/or training in industrialenvironments.

The following illustrative clauses describe certain embodiments of thepresent disclosure. The data collection system mentioned in thefollowing disclosure may be a local data collection system 102, a hostprocessing system 112 (e.g., using a cloud platform), or a combinationof a local system and a host system. In embodiments, a data collectionsystem or data collection and processing system is provided having theuse of an analog crosspoint switch for collecting data having variablegroups of analog sensor inputs and, in some embodiments, having IPfront-end-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio, multiplexer continuous monitoring alarmingfeatures, the use of distributed CPLD chips with a dedicated bus forlogic control of multiple MUX and data acquisition sections,high-amperage input capability using solid state relays and designtopology, power-down capability of at least one of an analog sensorchannel and of a component board, unique electrostatic protection fortrigger and vibration inputs, and/or precise voltage reference for A/Dzero reference.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having a phase-lock loopband-pass tracking filter for obtaining slow-speed RPMs and phaseinformation, digital derivation of phase relative to input and triggerchannels using on-board timers, a peak-detector for auto-scaling that isrouted into a separate analog-to-digital converter for peak detection,the routing of a trigger channel that is either raw or buffered intoother analog channels, the use of higher input oversampling fordelta-sigma A/D for lower sampling rate outputs to minimize AA filterrequirements, and/or the use of a CPLD as a clock-divider for adelta-sigma analog-to-digital converter to achieve lower sampling rateswithout the need for digital resampling.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having long blocks of dataat a high-sampling rate, as opposed to multiple sets of data taken atdifferent sampling rates, storage of calibration data with a maintenancehistory on-board card set, a rapid route creation capability usinghierarchical templates, intelligent management of data collection bands,and/or a neural net expert system using intelligent management of datacollection bands.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having use of a databasehierarchy in sensor data analysis, an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system, a graphical approach for back-calculation definition,proposed bearing analysis methods, torsional vibrationdetection/analysis utilizing transitory signal analysis, and/or improvedintegration using both analog and digital methods.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment, data acquisition parking features, a self-sufficient dataacquisition box, SD card storage, extended onboard statisticalcapabilities for continuous monitoring, the use of ambient, local andvibration noise for prediction, smart route changes based on incomingdata or alarms to enable simultaneous dynamic data for analysis orcorrelation, smart ODS and transfer functions, a hierarchicalmultiplexer, identification of sensor overload, and/or RF identificationand an inclinometer.

In embodiments, a data collection and processing system is providedhaving the use of an analog crosspoint switch for collecting data havingvariable groups of analog sensor inputs and having continuous ultrasonicmonitoring, cloud-based, machine pattern recognition based on the fusionof remote, analog industrial sensors, cloud-based, machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system,cloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices, on-device sensor fusion and data storagefor industrial IoT devices, a self-organizing data marketplace forindustrial IoT data, self-organization of data pools based onutilization and/or yield metrics, training AI models based onindustry-specific feedback, a self-organized swarm of industrial datacollectors, an IoT distributed ledger, a self-organizing collector, anetwork-sensitive collector, a remotely organized collector, aself-organizing storage for a multi-sensor data collector, aself-organizing network coding for multi-sensor data network, a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical, and/or sound outputs, heat maps displayingcollected data for AR/VR, and/or automatically tuned AR/VR visualizationof data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving IP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having at least oneof: multiplexer continuous monitoring alarming features; IP front-endsignal conditioning on a multiplexer for improved signal-to-noise ratio;the use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving at least one of: high-amperage input capability using solid staterelays and design topology; power-down capability of at least one analogsensor channel and of a component board; unique electrostatic protectionfor trigger and vibration inputs; precise voltage reference for A/D zeroreference; and a phase-lock loop band-pass tracking filter for obtainingslow-speed RPMs and phase information. In embodiments, a data collectionand processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving at least one of: digital derivation of phase relative to inputand trigger channels using on-board timers; a peak-detector forauto-scaling that is routed into a separate analog-to-digital converterfor peak detection; routing of a trigger channel that is either raw orbuffered into other analog channels; the use of higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize AA filter requirements; and the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling. Inembodiments, a data collection and processing system is provided havingIP front-end signal conditioning on a multiplexer for improvedsignal-to-noise ratio and having at least one of: long blocks of data ata high-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates; storage of calibration data with a maintenancehistory on-board card set; a rapid route creation capability usinghierarchical templates; intelligent management of data collection bands;and a neural net expert system using intelligent management of datacollection bands. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having at least oneof: use of a database hierarchy in sensor data analysis; an expertsystem GUI graphical approach to defining intelligent data collectionbands and diagnoses for the expert system; and a graphical approach forback-calculation definition. In embodiments, a data collection andprocessing system is provided having IP front-end signal conditioning ona multiplexer for improved signal-to-noise ratio and having at least oneof: proposed bearing analysis methods; torsional vibrationdetection/analysis utilizing transitory signal; improved integrationusing both analog and digital methods; adaptive scheduling techniquesfor continuous monitoring of analog data in a local environment; dataacquisition parking features; a self-sufficient data acquisition box;and SD card storage. In embodiments, a data collection and processingsystem is provided having IP front-end signal conditioning on amultiplexer for improved signal-to-noise ratio and having at least oneof: extended onboard statistical capabilities for continuous monitoring;the use of ambient, local, and vibration noise for prediction; smartroute changes based on incoming data or alarms to enable simultaneousdynamic data for analysis or correlation; smart ODS and transferfunctions; and a hierarchical multiplexer. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving at least one of: identification of sensor overload; RFidentification and an inclinometer; continuous ultrasonic monitoring;machine pattern recognition based on the fusion of remote, analogindustrial sensors; and cloud-based, machine pattern analysis of stateinformation from multiple analog industrial sensors to provideanticipated state information for an industrial system. In embodiments,a data collection and processing system is provided having IP front-endsignal conditioning on a multiplexer for improved signal-to-noise ratioand having at least one of: cloud-based policy automation engine forIoT, with creation, deployment, and management of IoT devices; on-devicesensor fusion and data storage for industrial IoT devices; aself-organizing data marketplace for industrial IoT data; andself-organization of data pools based on utilization and/or yieldmetrics. In embodiments, a data collection and processing system isprovided having IP front-end signal conditioning on a multiplexer forimproved signal-to-noise ratio and having at least one of: training AImodels based on industry-specific feedback; a self-organized swarm ofindustrial data collectors; an IoT distributed ledger; a self-organizingcollector; and a network-sensitive collector. In embodiments, a datacollection and processing system is provided having IP front-end signalconditioning on a multiplexer for improved signal-to-noise ratio andhaving at least one of: a remotely organized collector; aself-organizing storage for a multi-sensor data collector; aself-organizing network coding for multi-sensor data network; a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical, and/or sound outputs; heat maps displayingcollected data for AR/VR; and automatically tuned AR/VR visualization ofdata collected by a data collector.

In embodiments, a data collection and processing system is providedhaving multiplexer continuous monitoring alarming features. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having at leastone of: the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections; high-amperageinput capability using solid state relays and design topology;power-down capability of at least one of an analog sensor channel and/orof a component board; unique electrostatic protection for trigger andvibration inputs; and precise voltage reference for A/D zero reference.In embodiments, a data collection and processing system is providedhaving multiplexer continuous monitoring alarming features and having atleast one of: a phase-lock loop band-pass tracking filter for obtainingslow-speed RPMs and phase information; digital derivation of phaserelative to input and trigger channels using on-board timers; apeak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection; and routing of a triggerchannel that is either raw or buffered into other analog channels. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having at leastone of: the use of higher input oversampling for delta-sigma A/D forlower sampling rate outputs to minimize AA filter requirements; the useof a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling; long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates; storage ofcalibration data with a maintenance history on-board card set; and arapid route creation capability using hierarchical templates. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having at leastone of: intelligent management of data collection bands; a neural netexpert system using intelligent management of data collection bands; useof a database hierarchy in sensor data analysis; and an expert systemGUI graphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having at least one of: a graphical approach forback-calculation definition; proposed bearing analysis methods;torsional vibration detection/analysis utilizing transitory signalanalysis; and improved integration using both analog and digitalmethods. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving at least one of adaptive scheduling techniques for continuousmonitoring of analog data in a local environment; data acquisitionparking features; a self-sufficient data acquisition box; and SD cardstorage. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving at least one of: extended onboard statistical capabilities forcontinuous monitoring; the use of ambient, local and vibration noise forprediction; smart route changes based on incoming data or alarms toenable simultaneous dynamic data for analysis or correlation; and smartODS and transfer functions. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having at least one of: a hierarchicalmultiplexer; identification of sensor overload; RF identification, andan inclinometer; cloud-based, machine pattern recognition based on thefusion of remote, analog industrial sensors; and machine patternanalysis of state information from multiple analog industrial sensors toprovide anticipated state information for an industrial system. Inembodiments, a data collection and processing system is provided havingmultiplexer continuous monitoring alarming features and having at leastone of: cloud-based policy automation engine for IoT, with creation,deployment, and management of IoT devices; on-device sensor fusion anddata storage for industrial IoT devices; a self-organizing datamarketplace for industrial IoT data; self-organization of data poolsbased on utilization and/or yield metrics; and training AI models basedon industry-specific feedback. In embodiments, a data collection andprocessing system is provided having multiplexer continuous monitoringalarming features and having at least one of: a self-organized swarm ofindustrial data collectors; an IoT distributed ledger; a self-organizingcollector; a network-sensitive collector; and a remotely organizedcollector. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving at least one of: a self-organizing storage for a multi-sensordata collector; and a self-organizing network coding for multi-sensordata network. In embodiments, a data collection and processing system isprovided having multiplexer continuous monitoring alarming features andhaving at least one of: a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electrical,and/or sound outputs; heat maps displaying collected data for AR/VR; andautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having high-amperage inputcapability using solid state relays and design topology. In embodiments,a data collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having power-down capability of atleast one of an analog sensor channel and of a component board. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having uniqueelectrostatic protection for trigger and vibration inputs. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having precise voltagereference for A/D zero reference. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a phase-lock loop band-pass trackingfilter for obtaining slow-speed RPMs and phase information. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having digitalderivation of phase relative to input and trigger channels usingon-board timers. In embodiments, a data collection and processing systemis provided having the use of distributed CPLD chips with dedicated busfor logic control of multiple MUX and data acquisition sections andhaving a peak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection. In embodiments, a datacollection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having routing of a triggerchannel that is either raw or buffered into other analog channels. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having the use ofhigher input oversampling for delta-sigma. A/D for lower sampling rateoutputs to minimize AA filter requirements. In embodiments, a datacollection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having the use of a CPLD as aclock-divider for a delta-sigma analog-to-digital converter to achievelower sampling rates without the need for digital resampling. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having long blocks ofdata at a high-sampling rate as opposed to multiple sets of data takenat different sampling rates. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having storage of calibration data with amaintenance history on-board card set. In embodiments, a data collectionand processing system is provided having the use of distributed CPLDchips with dedicated bus for logic control of multiple MUX and dataacquisition sections and having a rapid route creation capability usinghierarchical templates. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a neural netexpert system using intelligent management of data collection bands. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having use of adatabase hierarchy in sensor data analysis. In embodiments, a datacollection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having an expert system GUIgraphical approach to defining intelligent data collection bands anddiagnoses for the expert system. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a graphical approach forback-calculation definition. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having proposed bearing analysis methods. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having torsionalvibration detection/analysis utilizing transitory signal analysis. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having improvedintegration using both analog and digital methods. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingdata acquisition parking features. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a self-sufficient data acquisition box.In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections and having SD cardstorage. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingextended onboard statistical capabilities for continuous monitoring. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having the use ofambient, local and vibration noise for prediction. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having smart route changes basedon incoming data or alarms to enable simultaneous dynamic data foranalysis or correlation. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having smart ODS and transfer functions. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a hierarchicalmultiplexer. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingidentification of sensor overload. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having RF identification and an inclinometer.In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections and havingcontinuous ultrasonic monitoring. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having cloud-based, machine pattern recognitionbased on fusion of remote, analog industrial sensors. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having cloud-based, machinepattern analysis of state information from multiple analog industrialsensors to provide anticipated state information for an industrialsystem. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and havingcloud-based policy automation engine for IoT, with creation, deployment,and management of IoT devices. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having on-device sensor fusion and data storagefor industrial IoT devices. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a self-organizing data marketplace forindustrial IoT data. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having self-organization of data pools based on utilizationand/or yield metrics. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having training AI models based on industry-specificfeedback. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and having aself-organized swarm of industrial data collectors. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having an IoT distributed ledger.In embodiments, a data collection and processing system is providedhaving the use of distributed CPLD chips with dedicated bus for logiccontrol of multiple MUX and data acquisition sections and having aself-organizing collector. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having a network-sensitive collector. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a remotelyorganized collector. In embodiments, a data collection and processingsystem is provided having the use of distributed CPLD chips withdedicated bus for logic control of multiple MUX and data acquisitionsections and having a self-organizing storage for a multi-sensor datacollector. In embodiments, a data collection and processing system isprovided having the use of distributed CPLD chips with dedicated bus forlogic control of multiple MUX and data acquisition sections and having aself-organizing network coding for multi-sensor data network. Inembodiments, a data collection and processing system is provided havingthe use of distributed CPLD chips with dedicated bus for logic controlof multiple MUX and data acquisition sections and having a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical, and/or sound outputs. In embodiments, adata collection and processing system is provided having the use ofdistributed CPLD chips with dedicated bus for logic control of multipleMUX and data acquisition sections and having heat maps displayingcollected data for AR/VR. In embodiments, a data collection andprocessing system is provided having the use of distributed CPLD chipswith dedicated bus for logic control of multiple MUX and dataacquisition sections and having automatically tuned AR/VR visualizationof data collected by a data collector.

In embodiments, a data collection and processing system is providedhaving one or more of high-amperage input capability using solid staterelays and design topology, power-down capability of at least one of ananalog sensor channel and of a component board, unique electrostaticprotection for trigger and vibration inputs, precise voltage referencefor A/D zero reference, a phase-lock loop band-pass tracking filter forobtaining slow-speed RPMs and phase information, digital derivation ofphase relative to input and trigger channels using on-board timers, apeak-detector for auto-scaling that is routed into a separateanalog-to-digital converter for peak detection, routing of a triggerchannel that is either raw or buffered into other analog channels, theuse of higher input oversampling for delta-sigma A/D for lower samplingrate outputs to minimize anti-aliasing (AA) filter requirements, the useof a CPLD as a clock-divider for a delta-sigma analog-to-digitalconverter to achieve lower sampling rates without the need for digitalresampling, long blocks of data at a high-sampling rate as opposed tomultiple sets of data taken at different sampling rates, storage ofcalibration data with a maintenance history on-board card set, a rapidroute creation capability using hierarchical templates, intelligentmanagement of data collection bands, a neural net expert system usingintelligent management of data collection bands, use of a databasehierarchy in sensor data analysis, an expert system GUI graphicalapproach to defining intelligent data collection bands and diagnoses forthe expert system, a graphical approach for back-calculation definition,proposed bearing analysis methods, torsional vibrationdetection/analysis utilizing transitory signal analysis, improvedintegration using both analog and digital methods, adaptive schedulingtechniques for continuous monitoring of analog data in a localenvironment, data acquisition parking features, a self-sufficient dataacquisition box, SD card storage, extended onboard statisticalcapabilities for continuous monitoring, the use of ambient, local, andvibration noise for prediction, smart route changes based on incomingdata or alarms to enable simultaneous dynamic data for analysis orcorrelation, smart ODS and transfer functions, a hierarchicalmultiplexer, identification of sensor overload, RF identification and aninclinometer, continuous ultrasonic monitoring, cloud-based, machinepattern recognition based on fusion of remote, analog industrialsensors, cloud-based, machine pattern analysis of state information frommultiple analog industrial sensors to provide anticipated stateinformation for an industrial system, cloud-based policy automationengine for IoT, with creation, deployment, and management of IoTdevices, on-device sensor fusion and data storage for industrial IoTdevices, a self-organizing data marketplace for industrial IoT data,self-organization of data pools based on utilization and/or yieldmetrics, training AI models based on industry-specific feedback, aself-organized swarm of industrial data collectors, an IoT distributedledger, a self-organizing collector, a network-sensitive collector, aremotely organized collector, a self-organizing storage for amulti-sensor data collector, a self-organizing network coding formulti-sensor data network, a wearable haptic user interface for anindustrial sensor data collector, with vibration, heat, electrical,and/or sound outputs, heat maps displaying collected data for AR/VR, orautomatically tuned AR/VR visualization of data collected by a datacollector.

In embodiments, a platform is provided having one or more ofcloud-based, machine pattern recognition based on fusion of remote,analog industrial sensors, cloud-based, machine pattern analysis ofstate information from multiple analog industrial sensors to provideanticipated state information for an industrial system, a cloud-basedpolicy automation engine for IoT, with creation, deployment, andmanagement of IoT devices, on-device sensor fusion and data storage forindustrial IoT devices, a self-organizing data marketplace forindustrial IoT data, self-organization of data pools based onutilization and/or yield metrics, training AI models based onindustry-specific feedback, a self-organized swarm of industrial datacollectors, an IoT distributed ledger, a self-organizing collector, anetwork-sensitive collector, a remotely organized collector, aself-organizing storage for a multi-sensor data collector, aself-organizing network coding for multi-sensor data network, a wearablehaptic user interface for an industrial sensor data collector, withvibration, heat, electrical, and/or sound outputs, heat maps displayingcollected data for AR/VR, or automatically tuned AR/VR visualization ofdata collected by a data collector.

With regard to FIG. 18, a range of existing data sensing and processingsystems with industrial sensing, processing, and storage systems 4500include a streaming data collector 4510 that may be configured to acceptdata in a range of formats as described herein. In embodiments, therange of formats can include a data format A 4520, a data format B 4522,a data format C 4524, and a data format D 4528 that may be sourced froma range of sensors. Moreover, the range of sensors can include aninstrument A 4540, an instrument B 4542, an instrument C 4544, and aninstrument D 4548. The streaming data collector 4510 may be configuredwith processing capabilities that enable access to the individualformats while leveraging the streaming, routing, self-organizingstorage, and other capabilities described herein.

FIG. 19 depicts methods and systems 4600 for industrial machine sensordata streaming collection, processing, and storage that facilitate useof a streaming data collector 4610 to collect and obtain data fromlegacy instruments 4620 and streaming instruments 4622. Legacyinstruments 4620 and their data methodologies may capture and providedata that is limited in scope, due to the legacy systems and acquisitionprocedures, such as existing data methodologies described above herein,to a particular range of frequencies and the like. The streaming datacollector 4610 may be configured to capture streaming instrument data4632 as well as legacy instrument data 4630. The streaming datacollector 4610 may also be configured to capture current streaminginstruments 4620 and legacy instruments 4622 and sensors using currentand legacy data methodologies. These embodiments may be useful intransition applications from the legacy instruments and processing tothe streaming instruments and processing that may be current or desiredinstruments or methodologies. In embodiments, the streaming datacollector 4610 may be configured to process the legacy instrument data4630 so that it can be stored compatibly with the streamed instrumentdata 4632. The streaming data collector 4610 may process or parse thestreamed instrument data 4632 based on the legacy instrument data 4630to produce at least one extraction of the streamed data 4642 that iscompatible with the legacy instrument data 4630 that can be processedinto translated legacy data 4640. In embodiments, extracted data 4650that can include extracted portions of translated legacy data 4652 andstreamed data 4654 may be stored in a format that facilitates access andprocessing by legacy instrument data processing and further processingthat can emulate legacy instrument data processing methods, and thelike. In embodiments, the portions of the translated legacy data 4652may also be stored in a format that facilitates processing withdifferent methods that can take advantage of the greater frequencies,resolution, and volume of data possible with a streaming instrument.

FIG. 20 depicts alternate embodiments descriptive of methods and systems4700 for industrial machine sensor data streaming, collection,processing, and storage that facilitate integration of legacyinstruments and processing. In embodiments, a streaming data collector4710 may be connected with an industrial machine 4712 and may include aplurality of sensors, such as streaming sensors 4720 and 4722 that maybe configured to sense aspects of the industrial machine 4712 associatedwith at least one moving part of the machine 4712. The sensors 4720 and4722 (or more) may communicate with one or more streaming devices 4740that may facilitate streaming data from one or more of the sensors tothe streaming data collector 4710. In embodiments, the industrialmachine 4712 may also interface with or include one or more legacyinstruments 4730 that may capture data associated with one or moremoving parts of the industrial machine 4712 and store that data into alegacy data storage facility 4732.

In embodiments, a frequency and/or resolution detection facility 4742may be configured to facilitate detecting information about legacyinstrument sourced data, such as a frequency range of the data or aresolution of the data, and the like. The detection facility 4742 mayoperate on data directly from the legacy instruments 4730 or from datastored in a legacy storage facility 4732. The detection facility 4742may communicate information detected about the legacy instruments 4730,its sourced data, and its stored data 4732, or the like to the streamingdata collector 4710. Alternatively, the detection facility 4742 mayaccess information, such as information about frequency ranges,resolution, and the like that characterizes the sourced data from thelegacy instrument 4730 and/or may be accessed from a portion of thelegacy storage facility 4732.

In embodiments, the streaming data collector 4710 may be configured withone or more automatic processors, algorithms, and/or other datamethodologies to match up information captured by the one or more legacyinstruments 4730 with a portion of data being provided by the one ormore streaming devices 4740 from the one or more industrial machines4712. Data from streaming devices 4740 may include a wider range offrequencies and resolutions than the sourced data of legacy instruments4730 and, therefore, filtering and other such functions can beimplemented to extract data from the streaming devices 4740 thatcorresponds to the sourced data of the legacy instruments 4730 inaspects such as frequency range, resolution, and the like. Inembodiments, the configured streaming data collector 4710 may produce aplurality of streams of data, including a stream of data that maycorrespond to the stream of data from the streaming device 4740 and aseparate stream of data that is compatible, in some aspects, with thelegacy instrument sourced data and the infrastructure to ingest andautomatically process it. Alternatively, the streaming data collector4710 may output data in modes other than as a stream, such as batches,aggregations, summaries, and the like.

Configured streaming data collector 4710 may communicate with a streamstorage facility 4764 for storing at least one of the data outputs fromthe streaming device 4710 and data extracted therefrom that may becompatible, in some aspects, with the sourced data of the legacyinstruments 4730. A legacy compatible output of the configured streamingdata collector 4710 may also be provided to a format adaptor facility4748, 4760 that may configure, adapt, reformat, and make otheradjustments to the legacy compatible data so that it can be stored in alegacy compatible storage facility 4762 so that legacy processingfacilities 4744 may execute data processing methods on data in thelegacy compatible storage facility 4762 and the like that are configuredto process the sourced data of the legacy instruments 4730. Inembodiments in which legacy compatible data is stored in the streamstorage facility 4764, legacy processing facility 4744 may alsoautomatically process this data after optionally being processed byformat adaptor 4760. By arranging the data collection, streaming,processing, formatting, and storage elements to provide data in a formatthat is fully compatible with legacy instrument sourced data, transitionfrom a legacy system can be simplified, and the sourced data from legacyinstruments can be easily compared to newly acquired data (with morecontent) without losing the legacy value of the sourced data from thelegacy instruments 4730.

FIG. 21 depicts alternate embodiments of the methods and systems 4800described herein for industrial machine sensor data streaming,collection, processing, and storage that may be compatible with legacyinstrument data collection and processing. In embodiments, processingindustrial machine sensed data may be accomplished in a variety of waysincluding aligning legacy and streaming sources of data, such as byaligning stored legacy and streaming data; aligning stored legacy datawith a stream of sensed data; and aligning legacy and streamed data asit is being collected. In embodiments, an industrial machine 4810 mayinclude, communicate with, or be integrated with one or more stream datasensors 4820 that may sense aspects of the industrial machine 4810 suchas aspects of one or more moving parts of the machine. The industrialmachine 4810 may also communicate with, include, or be integrated withone or more legacy data sensors 4830 that may sense similar aspects ofthe industrial machine 4810. In embodiments, the one or more legacy datasensors 4830 may provide sensed data to one or more legacy datacollectors 4840. The stream data sensors 4820 may produce an output thatencompasses all aspects of (i.e., a richer signal) and is compatiblewith sensed data from the legacy data sensors 4830. The stream datasensors 4820 may provide compatible data to the legacy data collector4840. By mimicking the legacy data sensors 4830 or their data streams,the stream data sensors 4820 may replace (or serve as suitable duplicatefor) one or more legacy data sensors, such as during an upgrade of thesensing and processing system of an industrial machine. Frequency range,resolution, and the like may be mimicked by the stream data so as toensure that all forms of legacy data are captured or can be derived fromthe stream data. In embodiments, format conversion, if needed, can alsobe performed by the stream data sensors 4820. The stream data sensors4820 may also produce an alternate data stream that is suitable forcollection by the stream data collector 4850. In embodiments, such analternate data stream may be a superset of the legacy data sensor datain at least one or more of: frequency range, resolution, duration ofsensing the data, and the like.

In embodiments, an industrial machine sensed data processing facility4860 may execute a wide range of sensed data processing methods, some ofwhich may be compatible with the data from legacy data sensors 4830 andmay produce outputs that may meet legacy sensed data processingrequirements. To facilitate use of a wide range of data processingcapabilities of processing facility 4860, legacy and stream data mayneed to be aligned so that a compatible portion of stream data may beextracted for processing with legacy compatible methods and the like. Inembodiments, FIG. 21 depicts three different techniques for aligningstream data to legacy data. A first alignment methodology 4862 includesaligning legacy data output by the legacy data collector 4840 withstream data output by the stream data collector 4850. As data isprovided by the legacy data collector 4840, aspects of the data may bedetected, such as resolution, frequency, duration, and the like, and maybe used as control for a processing method that identifies portions of astream of data from the stream data collector 4850 that are purposelycompatible with the legacy data. The processing facility 4860 may applyone or more legacy compatible methods on the identified portions of thestream data to extract data that can be easily compared to or referencedagainst the legacy data.

In embodiments, a second alignment methodology 4864 may involve aligningstreaming data with data from a legacy storage facility 4882. Inembodiments, a third alignment methodology 4868 may involve aligningstored stream data from a stream storage facility 4884 with legacy datafrom the legacy data storage facility 4882. In each of the methodologies4862, 4864, 4868, alignment data may be determined by processing thelegacy data to detect aspects such as resolution, duration, frequencyrange, and the like. Alternatively, alignment may be performed by analignment facility, such as facilities using methodologies 4862, 4864,4868 that may receive or may be configured with legacy data descriptiveinformation such as legacy frequency range, duration, resolution, andthe like.

In embodiments, an industrial machine sensing data processing facility4860 may have access to legacy compatible methods and algorithms thatmay be stored in a legacy data methodology storage facility 4880. Thesemethodologies, algorithms, or other data in the legacy algorithm storagefacility 4880 may also be a source of alignment information that couldbe communicated by the industrial machine sensed data processingfacility 4860 to the various alignment facilities having methodologies4862, 4864, 4868. By having access to legacy compatible algorithms andmethodologies, the data processing facility 4860 may facilitateprocessing legacy data, streamed data that is compatible with legacydata, or portions of streamed data that represent the legacy data toproduce legacy compatible analytics.

In embodiments, the data processing facility 4860 may execute a widerange of other sensed data processing methods, such as waveletderivations and the like, to produce streamed data analytics 4892. Inembodiments, the streaming data collector 102, 4510, 4610, 4710 (FIGS.3, 6, 18, 19, 20) or data processing facility 4860 may include portablealgorithms, methodologies, and inputs that may be defined and extractedfrom data streams. In many examples, a user or enterprise may alreadyhave existing and effective methods related to analyzing specific piecesof machinery and assets. These existing methods could be imported intothe configured streaming data collector 102, 4510, 4610, 4710 or thedata processing facility 4860 as portable algorithms or methodologies.Data processing, such as described herein for the configured streamingdata collector 102, 4510, 4610, 4710 may also match an algorithm ormethodology to a situation, then extract data from a stream to match tothe data methodology from the legacy acquisition or legacy acquisitiontechniques. In embodiments, the streaming data collector 102, 4510,4610, 4710 may be compatible with many types of systems and may becompatible with systems having varying degrees of criticality.

Exemplary industrial machine deployments of the methods and systemsdescribed herein are now described. An industrial machine may be a gascompressor. In an example, a gas compressor may operate an oil pump on avery large turbo machine, such as a very large turbo machine thatincludes 10,000 HP motors. The oil pump may be a highly critical systemas its failure could cause an entire plant to shut down. The gascompressor in this example may run four stages at a very high frequency,such as 36,000 RPM, and may include tilt pad bearings that ride on anoil film. The oil pump in this example may have roller bearings, suchthat if an anticipated failure is not being picked up by a user, the oilpump may stop running, and the entire turbo machine would fail.Continuing with this example, the streaming data collector 102, 4510,4610, 4710 may collect data related to vibrations, such as casingvibration and proximity probe vibration. Other bearings industrialmachine examples may include generators, power plants, boiler feedpumps, fans, forced draft fans, induced draft fans, and the like. Thestreaming data collector 102, 4510, 4610, 4710 for a bearings systemused in the industrial gas industry may support predictive analysis onthe motors, such as that performed by model-based expert systems—forexample, using voltage, current, and vibration as analysis metrics.

Another exemplary industrial machine deployment may be a motor and thestreaming data collector 102, 4510, 4610, 4710 that may assist in theanalysis of a motor by collecting voltage and current data on the motor,for example.

Yet another exemplary industrial machine deployment may include oilquality sensing. An industrial machine may conduct oil analysis, and thestreaming data collector 102, 4510, 4610, 4710 may assist in searchingfor fragments of metal in oil, for example.

The methods and systems described herein may also be used in combinationwith model-based systems. Model-based systems may integrate withproximity probes. Proximity probes may be used to sense problems withmachinery and shut machinery down due to sensed problems. A model-basedsystem integrated with proximity probes may measure a peak waveform andsend a signal that shuts down machinery based on the peak waveformmeasurement.

Enterprises that operate industrial machines may operate in many diverseindustries. These industries may include industries that operatemanufacturing lines, provide computing infrastructure, support financialservices, provide HVAC equipment, and the like. These industries may behighly sensitive to lost operating time and the cost incurred due tolost operating time. HVAC equipment enterprises in particular may beconcerned with data related to ultrasound, vibration, IR, and the like,and may get much more information about machine performance related tothese metrics using the methods and systems of industrial machine senseddata streaming collection than from legacy systems.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for capturing a plurality ofstreams of sensed data from sensors deployed to monitor aspects of anindustrial machine associated with at least one moving part of themachine; at least one of the streams containing a plurality offrequencies of data. The method may include identifying a subset of datain at least one of the multiple streams that corresponds to datarepresenting at least one predefined frequency. The at least onepredefined frequency is represented by a set of data collected fromalternate sensors deployed to monitor aspects of the industrial machineassociated with the at least one moving part of the machine. The methodmay further include processing the identified data with a dataprocessing facility that processes the identified data with datamethodologies configured to be applied to the set of data collected fromalternate sensors. Lastly, the method may include storing the at leastone of the streams of data, the identified subset of data, and a resultof processing the identified data in an electronic data set.

The methods and systems may include a method for applying data capturedfrom sensors deployed to monitor aspects of an industrial machineassociated with at least one moving part of the machine, the datacaptured with predefined lines of resolution covering a predefinedfrequency range, to a frequency matching facility that identifies asubset of data streamed from other sensors deployed to monitor aspectsof the industrial machine associated with at least one moving part ofthe machine, the streamed data comprising a plurality of lines ofresolution and frequency ranges, the subset of data identifiedcorresponding to the lines of resolution and predefined frequency range.This method may include storing the subset of data in an electronic datarecord in a format that corresponds to a format of the data capturedwith predefined lines of resolution, and signaling to a data processingfacility the presence of the stored subset of data. This method mayoptionally include processing the subset of data with at least one ofalgorithms, methodologies, models, and pattern recognizers thatcorresponds to algorithms, methodologies, models, and patternrecognizers associated with processing the data captured with predefinedlines of resolution covering a predefined frequency range.

The methods and systems may include a method for identifying a subset ofstreamed sensor data. The sensor data is captured from sensors deployedto monitor aspects of an industrial machine associated with at least onemoving part of the machine. The subset of streamed sensor data is atpredefined lines of resolution for a predefined frequency range. Themethod includes establishing a first logical route for communicatingelectronically between a first computing facility performing theidentifying and a second computing facility. The identified subset ofthe streamed sensor data is communicated exclusively over theestablished first logical route when communicating the subset ofstreamed sensor data from the first facility to the second facility.This method may further include establishing a second logical route forcommunicating electronically between the first computing facility andthe second computing facility for at least one portion of the streamedsensor data that is not the identified subset. This method may furtherinclude establishing a third logical route for communicatingelectronically between the first computing facility and the secondcomputing facility for at least one portion of the streamed sensor datathat includes the identified subset and at least one other portion ofthe data not represented by the identified subset.

The methods and systems may include a first data sensing and processingsystem that captures first data from a first set of sensors deployed tomonitor aspects of an industrial machine associated with at least onemoving part of the machine, the first data covering a set of lines ofresolution and a frequency range. This system may include a second datasensing and processing system that captures and streams a second set ofdata from a second set of sensors deployed to monitor aspects of theindustrial machine associated with at least one moving part of themachine, the second data covering a plurality of lines of resolutionthat includes the set of lines of resolution and a plurality offrequencies that includes the frequency range. The system may enable:(1) selecting a portion of the second data that corresponds to the setof lines of resolution and the frequency range of the first data; and(2) processing the selected portion of the second data with the firstdata sensing and processing system.

The methods and systems may include a method for automaticallyprocessing a portion of a stream of sensed data. The sensed datareceived from a first set of sensors deployed to monitor aspects of anindustrial machine associated with at least one moving part of themachine in response to an electronic data structure that facilitatesextracting a subset of the stream of sensed data that corresponds to aset of sensed data received from a second set of sensors deployed tomonitor the aspects of the industrial machine associated with the atleast one moving part of the machine. The set of sensed data isconstrained to a frequency range. The stream of sensed data includes arange of frequencies that exceeds the frequency range of the set ofsensed data. The processing comprises executing data methodologies on aportion of the stream of sensed data that is constrained to thefrequency range of the set of sensed data. The data methodologies areconfigured to process the set of sensed data.

The methods and systems may include a method for receiving first datafrom sensors deployed to monitor aspects of an industrial machineassociated with at least one moving part of the machine. This method mayfurther include: (1) detecting at least one of a frequency range andlines of resolution represented by the first data, and (2) receiving astream of data from sensors deployed to monitor the aspects of theindustrial machine associated with the at least one moving part of themachine. The stream of data includes: a plurality of frequency rangesand a plurality of lines of resolution that exceeds the frequency rangeand the lines of resolution represented by the first data; extracting aset of data from the stream of data that corresponds to at least one ofthe frequency range and the lines of resolution represented by the firstdata; and processing the extracted set of data with a data processingmethod that is configured to process data within the frequency range andwithin the lines of resolution of the first data.

The methods and systems disclosed herein may include, connect to, or beintegrated with a data acquisition instrument and in the manyembodiments, FIG. 22 shows methods and systems 5000 that includes a dataacquisition (DAQ) streaming instrument 5002 also known as an SDAQ. Inembodiments, output from sensors 5010, 5012, 5014 may be of varioustypes including vibration, temperature, pressure, ultrasound and so on.In my many examples, one of the sensors may be used. In furtherexamples, many of the sensors may be used and their signals may be usedindividually or in predetermined combinations and/or at predeterminedintervals, circumstances, setups, and the like.

In embodiments, the output signals from the sensors 5010, 5012, 5014 maybe fed into instrument inputs 5020, 5022, 5024 of the DAQ instrument5002 and may be configured with additional streaming capabilities 5028.By way of these many examples, the output signals from the sensors 5010,5012, 5014, or more as applicable, may be conditioned as an analogsignal before digitization with respect to at least scaling andfiltering. The signals may then be digitized by an analog-to-digitalconverter 5030. The signals received from all relevant channels (i.e.,one or more channels are switched on manually, by alarm, by route, andthe like) may be simultaneously sampled at a predetermined ratesufficient to perform the maximum desired frequency analysis that may beadjusted and readjusted as needed or otherwise held constant to ensurecompatibility or conformance with other relevant datasets. Inembodiments, the signals are sampled for a relatively long time andgap-free as one continuous stream so as to enable furtherpost-processing at lower sampling rates with sufficient individualsampling.

In embodiments, data may be streamed from a collection of points andthen the next set of data may be collected from additional pointsaccording to a prescribed sequence, route, path, or the like. In manyexamples, the sensors 5010, 5012, 5014 or more may be moved to the nextlocation according to the prescribed sequence, route, pre-arrangedconfigurations, or the like. In certain examples, not all of the sensor5010, 5012, 5014 may move and therefore some may remain fixed in placeand used for detection of reference phase or the like.

In embodiments, a multiplex (mux) 5032 may be used to switch to the nextcollection of points, to a mixture of the two methods or collectionpatterns that may be combined, other predetermined routes, and the like.The multiplexer 5032 may be stackable so as to be laddered andeffectively accept more channels than the DAQ instrument 5002 provides.In examples, the DAQ instrument 5002 may provide eight channels whilethe multiplexer 5032 may be stacked to supply 32 channels. Furthervariations are possible with one more multiplexers. In embodiments, themultiplexer 5032 may be fed into the DAQ instrument 5002 through aninstrument input 5034. In embodiments, the DAQ instrument 5002 mayinclude a controller 5038 that may take the form of an onboardcontroller, a PC, other connected devices, network based services, andcombinations thereof.

In embodiments, the sequence and panel conditions used to govern thedata collection process may be obtained from the multimedia probe (MMP)and probe control, sequence and analytical (PCSA) information store5040. In embodiments, the information store 5040 may be onboard the DAQinstrument 5002. In embodiments, contents of the information store 5040may be obtained through a cloud network facility, from other DAQinstruments, from other connected devices, from the machine beingsensed, other relevant sources, and combinations thereof. Inembodiments, the information store 5040 may include such items as thehierarchical structural relationships of the machine, e.g., a machinecontains predetermined pieces of equipment, each of which may containone or more shafts and each of those shafts may have multiple associatedbearings. Each of those types of bearings may be monitored by specifictypes of transducers or probes, according to one or more specificprescribed sequences (paths, routes, and the like) and with one or morespecific panel conditions that may be set on the one or more DAQinstruments 5002. By way of this example, the panel conditions mayinclude hardware specific switch settings or other collectionparameters. In many examples, collection parameters include but are notlimited to a sampling rate, AC/DC coupling, voltage range and gain,integration, high and low pass filtering, anti-aliasing filtering, ICP™transducers and other integrated-circuit piezoelectric transducers, 4-20mA loop sensors, and the like. In embodiments, the information store5040 may also include machinery specific features that may be importantfor proper analysis such as gear teeth for a gear, number blades in apump impeller, number of motor rotor bars, bearing specific parametersnecessary for calculating bearing frequencies, revolution per minutesinformation of all rotating elements and multiples of those RPM ranges,and the like. Information in the information store may also be used toextract stream data 5050 for permanent storage.

Based on directions from the DAQ API software 5052, digitized waveformsmay be uploaded using DAQ driver services 5054 of a driver onboard theDAQ instrument 5002. In embodiments, data may then be fed into a rawdata server 5058 which may store the stream data 5050 in a stream datarepository 5060. In embodiments, this data storage area is typicallymeant for storage until the data is copied off of the DAQ instrument5002 and verified. The DAQ API 5052 may also direct the local datacontrol application 5062 to extract and process the recently obtainedstream data 5050 and convert it to the same or lower sampling rates ofsufficient length to effect one or more desired resolutions. By way ofthese examples, this data may be converted to spectra, averaged, andprocessed in a variety of ways and stored, at least temporarily, asextracted/processed (EP) data 5064. It will be appreciated in light ofthe disclosure that legacy data may require its own sampling rates andresolution to ensure compatibility and often this sampling rate may notbe integer proportional to the acquired sampling rate. It will also beappreciated in light of the disclosure that this may be especiallyrelevant for order-sampled data whose sampling frequency is relateddirectly to an external frequency (typically the running speed of themachine or its local componentry) rather than the more-standard samplingrates employed by the internal crystals, clock functions, or the like ofthe DAQ instrument (e.g., values of Fmax of 100, 200, 500, 1K, 2K, 5K,10K, 20K, and so on).

In embodiments, the extract/process (EP) align module 5068 of the localdata control application 5062 may be able to fractionally adjust thesampling rates to these non-integer ratio rates satisfying an importantrequirement for making data compatible with legacy systems. Inembodiments, fractional rates may also be converted to integer ratiorates more readily because the length of the data to be processed may beadjustable. It will be appreciated in light of the disclosure that ifthe data was not streamed and just stored as spectra with the standardor predetermined Fmax, it may be impossible in certain situations toconvert it retroactively and accurately to the order-sampled data. Itwill also be appreciated in light of the disclosure that internalidentification issues may also need to be reconciled. In many examples,stream data may be converted to the proper sampling rate and resolutionas described and stored (albeit temporarily) in an EP legacy datarepository 5070 to ensure compatibility with legacy data.

To support legacy data identification issues, a user input module 5072is shown in many embodiments should there be no automated process(whether partially or wholly) for identification translation. In suchexamples, one or more legacy systems (i.e., pre-existing dataacquisition) may be characterized in that the data to be imported is ina fully standardized format such as a Mimosa™ format, and other similarformats. Moreover, sufficient indentation of the legacy data and/or theone or more machines from which the legacy data was produced may berequired in the completion of an identification mapping table 5074 toassociate and link a portion of the legacy data to a portion of thenewly acquired streamed data 5050. In many examples, the end user and/orlegacy vendor may be able to supply sufficient information to completeat least a portion of a functioning identification (ID) mapping table5074 and therefore may provide the necessary database schema for the rawdata of the legacy system to be used for comparison, analysis, andmanipulation of newly streamed data 5050.

In embodiments, the local data control application 5062 may also directstreaming data as well as extracted/processed (EP) data to a cloudnetwork facility 5080 via wired or wireless transmission. From the cloudnetwork facility 5080 other devices may access, receive, and maintaindata including the data from a master raw data server (MRDS) 5082. Themovement, distribution, storage, and retrieval of data remote to the DAQinstrument 5002 may be coordinated by the cloud data management services(“CDMS”) 5084.

FIG. 23 shows additional methods and systems that include the DAQinstrument 5002 accessing related cloud based services. In embodiments,the DAQ API 5052 may control the data collection process as well as itssequence. By way of these examples, the DAQ API 5052 may provide thecapability for editing processes, viewing plots of the data, controllingthe processing of that data, viewing the output data in all its myriadforms, analyzing this data including expert analysis, and communicatingwith external devices via the local data control application 5062 andwith the CDMS 5084 via the cloud network facility 5080. In embodiments,the DAQ API 5052 may also govern the movement of data, its filtering, aswell as many other housekeeping functions.

In embodiments, an expert analysis module 5100 may generate reports 5102that may use machine or measurement point specific information from theinformation store 5040 to analyze the stream data 5050 using a streamdata analyzer module 5104 and the local data control application 5062with the extract/process (“EP”) align module 5068. In embodiments, theexpert analysis module 5100 may generate new alarms or ingest alarmsettings into an alarms module 5108 that is relevant to the stream data5050. In embodiments, the stream data analyzer module 5104 may provide amanual or automated mechanism for extracting meaningful information fromthe stream data 5050 in a variety of plotting and report formats. Inembodiments, a supervisory control of the expert analysis module 5100 isprovided by the DAQ API 5052. In further examples, the expert analysismodule 5100 may be supplied (wholly or partially) via the cloud networkfacility 5080. In many examples, the expert analysis module 5100 via thecloud may be used rather than a locally-deployed expert analysis module5100 for various reasons such as using the most up-to-date softwareversion, more processing capability, a bigger volume of historical datato reference, and so on. In many examples, it may be important that theexpert analysis module 5100 be available when an internet connectioncannot be established so having this redundancy may be crucial forseamless and time efficient operation. Toward that end, many of themodular software applications and databases available to the DAQinstrument 5002 where applicable may be implemented with systemcomponent redundancy to provide operational robustness to provideconnectivity to cloud services when needed but also operate successfullyin isolated scenarios where connectivity is not available and sometimenot available purposefully to increase security and the like.

In embodiments, the DAQ instrument acquisition may require a real timeoperating system (“RTOS”) for the hardware especially for streamedgap-five data that is acquired by a PC. In some instances, therequirement for a RTOS may result in (or may require) expensive customhardware and software capable of running such a system. In manyembodiments, such expensive custom hardware and software may be avoidedand an RTOS may be effectively and sufficiently implemented using astandard Windows™ operating systems or similar environments includingthe system interrupts in the procedural flow of a dedicated applicationincluded in such operating systems.

The methods and systems disclosed herein may include, connect to, or beintegrated with one or more DAQ instruments and in the many embodiments,FIG. 24 shows methods and systems 5150 that include the DAQ instrument5002 (also known as a streaming DAQ or an SDAQ). In embodiments, the DAQinstrument 5002 may effectively and sufficiently implement an RTOS usingstandard windows operating system (or other similar personal computingsystems) that may include a software driver configured with a First In,First Out (FIFO) memory area 5152. The FIFO memory area 5152 may bemaintained and hold information for a sufficient amount of time tohandle a worst-case interrupt that it may face from the local operatingsystem to effectively provide the RTOS. In many examples, configurationson a local personal computer or connected device may be maintained tominimize operating system interrupts. To support this, theconfigurations may be maintained, controlled, or adjusted to eliminate(or be isolated from) any exposure to extreme environments whereoperating system interrupts may become an issue. In embodiments, the DAQinstrument 5002 may produce a notification, alarm, message, or the liketo notify a user when any gap errors are detected. In these manyexamples, such errors may be shown to be rare and even if they occur,the data may be adjusted knowing when they occurred should such asituation arise.

In embodiments, the DAQ instrument 5002 may maintain a sufficientlylarge FIFO memory area 5152 that may buffer the incoming data so as tobe not affected by operating system interrupts when acquiring data. Itwill be appreciated in light of the disclosure that the predeterminedsize of the FIFO memory area 5152 may be based on operating systeminterrupts that may include Windows system and application functionssuch as the writing of data to Disk or SSD, plotting, GUI interactionsand standard Windows tasks, low-level driver tasks such as servicing theDAQ hardware and retrieving the data in bursts, and the like.

In embodiments, the computer, controller, connected device or the likethat may be included in the DAQ instrument 5002 may be configured toacquire data from the one or more hardware devices over a USB port,firewire, ethernet, or the like. In embodiments, the DAQ driver services5054 may be configured to have data delivered to it periodically so asto facilitate providing a channel specific FIFO memory buffer that maybe configured to not miss data, i.e., it is gap-free. In embodiments,the DAQ driver services 5054 may be configured so as to maintain an evenlarger (than the device) channel specific FIFO area 5152 that it fillswith new data obtained from the device. In embodiments, the DAQ driverservices 5054 may be configured to employ a further process in that theraw data server 5058 may take data from the FIFO 5110 and may write itas a contiguous stream to non-volatile storage areas such as the streamdata repository 5060 that may be configured as one or more disk drives,SSDs, or the like. In embodiments, the FIFO 5110 may be configured toinclude a starting and stopping marker or pointer to mark where thelatest most current stream was written. By way of these examples, a FIFOend marker 5114 may be configured to mark the end of the most currentdata until it reaches the end of the spooler and then wraps aroundconstantly cycling around. In these examples, there is always onemegabyte (or other configured capacities) of the most current dataavailable in the FIFO 5110 once the spooler fills up. It will beappreciated in light of the disclosure that further configurations ofthe FIFO memory area may be employed. In embodiments, the DAQ driverservices 5054 may be configured to use the DAQ API 5052 to pipe the mostrecent data to a high-level application for processing, graphing andanalysis purposes. In some examples, it is not required that this databe gap-free but even in these instances, it is helpful to identify andmark the gaps in the data. Moreover, these data updates may beconfigured to be frequent enough so that the user would perceive thedata as live. In the many embodiments, the raw data is flushed tonon-volatile storage without a gap at least for the prescribed amount oftime and examples of the prescribed amount of time may be about thirtyseconds to over four hours. It will be appreciated in light of thedisclosure that many pieces of equipment and their components maycontribute to the relative needed duration of the stream of gap-freedata and those durations may be over four hours when relatively lowspeeds are present in large numbers, when non-periodic transientactivity is occurring on a relatively long time frame, when duty cycleonly permits operation in relevant ranges for restricted durations andthe like.

With reference to FIG. 23, the stream data analyzer module 5104 mayprovide for the manual or extraction of information from the data streamin a variety of plotting and report formats. In embodiments, resampling,filtering (including anti-aliasing), transfer functions, spectrumanalysis, enveloping, averaging, peak detection functionality, as wellas a host of other signal processing tools, may be available for theanalyst to analyze the stream data and to generate a very large array ofsnapshots. It will be appreciated in light of the disclosure that muchlarger arrays of snapshots are created than ever would have beenpossible by scheduling the collection of snapshots beforehand, i.e.,during the initial data acquisition for the measurement point inquestion.

FIG. 25 depicts a display 5200 whose viewable content 5202 may beaccessed locally or remotely, wholly or partially. In many embodiments,the display 5200 may be part of the DAQ instrument 5002, may be part ofthe PC or connected device 5038 that may be part of the DAQ instrument5002, or its viewable content 5202 may be viewable from associatednetwork connected displays. In further examples, the viewable content5202 of the display 5200 or portions thereof may be ported to one ormore relevant network addresses. In the many embodiments, the viewablecontent 5202 may include a screen 5204 that shows, for example, anapproximately two-minute data stream 5208 may be collected at a samplingrate of 25.6 kHz for four channels 5220, 5222, 5224, 5228,simultaneously. By way of these examples and in these configurations,the length of the data may be approximately 3.1 megabytes. It will beappreciated in light of the disclosure that the data stream (includingeach of its four channels or as many as applicable) may be replayed insome aspects like a magnetic tape recording (e.g. a reel-to-reel or acassette) with all of the controls normally associated with playbacksuch as forward 5230, fast forward, backward 5232, fast rewind, stepback, step forward, advance to time point, retreat to time point,beginning 5234, end, 5238, play 5240, stop 5242, and the like.Additionally, the playback of the data stream may further be configuredto set a width of the data stream to be shown as a contiguous subset ofthe entire stream. In the example with a two-minute data stream, theentire two minutes may be selected by the “select all” button 5244, orsome subset thereof may be selected with the controls on the screen 5204or that may be placed on the screen 5204 by configuring the display 5200and the DAQ instrument 5002. In this example, the “process selecteddata” button 5250 on the screen 5204 may be selected to commit to aselection of the data stream.

FIG. 26 depicts the many embodiments that include a screen 5250 on thedisplay 5200 that shows results of selecting all of the data for thisexample. In embodiments, the screen 5250 in FIG. 26 may provide the sameor similar playback capabilities as what is depicted on the screen 5204shown in FIG. 25 but also includes resampling capabilities, waveformdisplays, and spectrum displays. In light of the disclosure, it will beappreciated that this functionality may permit the user to choose inmany situations any Fmax less than that supported by the originalstreaming sampling rate. In embodiments, any section of any size may beselected and further processing, analytics, and tools for viewing anddissecting the data may be provided. In embodiments, the screen 5250 mayinclude four windows 5252, 5254, 5258, 5260 that show the stream datafrom the four channels 5220, 5222, 5224, 5228 of FIG. 25. Inembodiments, the screen 5250 may also include offset and overlapcontrols 5262, resampling controls 5264, and other similar controls.

In many examples, any one of many transfer functions may be establishedbetween any two channels, such as the two channels 5280, 5282 that maybe shown on a screen 5284, shown on the display 5200, as depicted inFIG. 27. The selection of the two channels 5280, 5282 on the screen 5284may permit the user to depict the output of the transfer function on anyof the screens including screen 5284 and screen 5204.

In embodiments, FIG. 28 shows a high-resolution spectrum screen 5300 onthe display 5200 with a waveform view 5302, full cursor control 5304 anda peak extraction view 5308. In these examples, the peak extraction view5308 may be configured with a resolved configuration 5310 that may beconfigured to provide enhanced amplitude and frequency accuracy and mayuse spectral sideband energy distribution. The peak extraction view 5308may also be configured with averaging 5312, phase and cursor vectorinformation 5314, and the like.

In embodiments, FIG. 29 shows an enveloping screen 5350 on the display5200 with a waveform view 5352, and a spectral format view 5354. Theviews 5352, 5354 on the enveloping screen 5350 may display modulationfrom the signal in both waveform and spectral formats. In embodiments,FIG. 30 shows a relative phase screen 5380 on the display 5200 with fourphase views 5382, 5384, 5388, 5390. The four phase views 5382, 5384,5388, 5390 relate to the on spectrum the enveloping screen 5350 that maydisplay modulation from the signal in waveform format in view 5352 andspectral format in view 5354. In embodiments, the reference channelcontrol 5392 may be selected to use channel four as a reference channelto determine relative phase between each of the channels.

It will be appreciated in light of the disclosure that the samplingrates of vibration data of up to 100 kHz (or higher in some scenarios)may be utilized for non-vibration sensors as well. In doing so, it willfurther be appreciated in light of the disclosure that stream data insuch durations at these sampling rates may uncover new patterns to beanalyzed due in no small part that many of these types of sensors havenot been utilized in this manner. It will also be appreciated in lightof the disclosure that different sensors used in machinery conditionmonitoring may provide measurements more akin to static levels ratherthan fast-acting dynamic signals. In some cases, faster response timetransducers may have to be used prior to achieving the faster samplingrates.

In many embodiments, sensors may have a relatively static output such astemperature, pressure, or flow but may still be analyzed with thedynamic signal processing system and methodologies as disclosed herein.It will be appreciated in light of the disclosure that the time scale,in many examples, may be slowed down. In many examples, a collection oftemperature readings collected approximately every minute for over twoweeks may be analyzed for their variation solely or in collaboration orin fusion with other relevant sensors. By way of these examples, thedirect current level or average level may be omitted from all thereadings (e.g., by subtraction) and the resulting delta measurements maybe processed (e.g., through a Fourier transform). From these examples,resulting spectral lines may correlate to specific machinery behavior orother symptoms present in industrial system processes. In furtherexamples, other techniques include enveloping that may look formodulation, wavelets that may look for spectral patterns that last onlyfor a short time (e.g., bursts), cross-channel analysis to look forcorrelations with other sensors including vibration, and the like.

FIG. 31 shows a DAQ instrument 5400 that may be integrated with one ormore analog sensors 5402 and endpoint nodes 5404 to provide a streamingsensor 5410 or smart sensors that may take in analog signals and thenprocess and digitize them, and then transmit them to one or moreexternal monitoring systems 5412 in the many embodiments that may beconnected to, interfacing with, or integrated with the methods andsystems disclosed herein. The monitoring system 5412 may include astreaming hub server 5420 that may communicate with the CDMS 5084. Inembodiments, the CDMS 5084 may contact, use, and integrate with clouddata 5430 and cloud services 5432 that may be accessible through one ormore cloud network facilities 5080. In embodiments, the streaming hubserver 5420 may connect with another streaming sensor 5440 that mayinclude a DAQ instrument 5442, an endpoint node 5444, and the one ormore analog sensors such as analog sensor 5448. The steaming hub server5420 may connect with other streaming sensors such as the streamingsensor 5460 that may include a DAQ instrument 5462, an endpoint node5464, and the one or more analog sensors such as analog sensor 5468.

In embodiments, there may be additional streaming hub servers such asthe steaming hub server 5480 that may connect with other streamingsensors such as the streaming sensor 5490 that may include a DAQinstrument 5492, an endpoint node 5494, and the one or more analogsensors such as analog sensor 5498. In embodiments, the streaming hubserver 5480 may also connect with other streaming sensors such as thestreaming sensor 5500 that may include a DAQ instrument 5502, anendpoint node 5504, and the one or more analog sensors such as analogsensor 5508. In embodiments, the transmission may include averagedoverall levels and in other examples may include dynamic signal sampledat a prescribed and/or fixed rate. In embodiments, the streaming sensors5410, 5440, 5460, 5490, and 5500 may be configured to acquire analogsignals and then apply signal conditioning to those analog signalsincluding coupling, averaging, integrating, differentiating, scaling,filtering of various kinds, and the like. The streaming sensors 5410,5440, 5460, 5490, and 5500 may be configured to digitize the analogsignals at an acceptable rate and resolution (number of bits) and toprocess further the digitized signal when required. The streamingsensors 5410, 5440, 5460, 5490, and 5500 may be configured to transmitthe digitized signals at pre-determined, adjustable, and re-adjustablerates. In embodiments, the streaming sensors 5410, 5440, 5460, 5490, and5500 are configured to acquire, digitize, process, and transmit data ata sufficient effective rate so that a relatively consistent stream ofdata may be maintained for a suitable amount of time so that a largenumber of effective analyses may be shown to be possible. In the manyembodiments, there would be no gaps in the data stream and the length ofdata should be relatively long, ideally for an unlimited amount of time,although practical considerations typically require ending the stream.It will be appreciated in light of the disclosure that this longduration data stream with effectively no gap in the stream is incontrast to the more commonly used burst collection where data iscollected for a relatively short period of time (i.e., a short burst ofcollection), followed by a pause, and then perhaps another burstcollection and so on. In the commonly used collections of data collectedover noncontiguous bursts, data would be collected at a slow rate forlow frequency analysis and high frequency for high frequency analysis.In many embodiments of the present disclosure, in contrast, thestreaming data is being collected (i) once, (ii) at the highest usefuland possible sampling rate, and (iii) for a long enough time that lowfrequency analysis may be performed as well as high frequency. Tofacilitate the collection of the streaming data, enough storage memorymust be available on the one or more streaming sensors such as thestreaming sensors 5410, 5440, 5460, 5490, 5500 so that new data may beoff-loaded externally to another system before the memory overflows. Inembodiments, data in this memory would be stored into and accessed from“First-In, First-Out” (“FIFO”) mode. In these examples, the memory witha FIFO area may be a dual port so that the sensor controller may writeto one part of it while the external system reads from a different part.In embodiments, data flow traffic may be managed by semaphore logic.

It will be appreciated in light of the disclosure that vibrationtransducers that are larger in mass will have a lower linear frequencyresponse range because the natural resonance of the probe is inverselyrelated to the square root of the mass and will be lowered. Toward thatend, a resonant response is inherently non-linear and so a transducerwith a lower natural frequency will have a narrower linear passbandfrequency response. It will also be appreciated in light of thedisclosure that above the natural frequency the amplitude response ofthe sensor will taper off to negligible levels rendering it even moreunusable. With that in mind, high frequency accelerometers, for thisreason, tend to be quite small in mass, to the order of half of a gram.It will also be appreciated in light of the disclosure that adding therequired signal processing and digitizing electronics required forstreaming may, in certain situations, render the sensors incapable inmany instances of measuring high-frequency activity.

In embodiments, streaming hubs such as the streaming hubs 5420, 5480 mayeffectively move the electronics required for streaming to an externalhub via cable. It will be appreciated in light of the disclosure thatthe streaming hubs may be located virtually next to the streamingsensors or up to a distance supported by the electronic drivingcapability of the hub. In instances where an internet cache protocol(“ICP”) is used, the distance supported by the electronic drivingcapability of the hub would be anywhere from 100 to 1000 feet (30.5 to305 meters) based on desired frequency response, cable capacitance, andthe like. In embodiments, the streaming hubs may be positioned in alocation convenient for receiving power as well as connecting to anetwork (be it LAN or WAN). In embodiments, other power options wouldinclude solar, thermal as well as energy harvesting. Transfer betweenthe streaming sensors and any external systems may be wireless or wiredand may include such standard communication technologies as 802.11 and900 MHz wireless systems, Ethernet, USB, firewire and so on.

With reference to FIG. 22, the many examples of the DAQ instrument 5002include embodiments where data that may be uploaded from the local datacontrol application 5062 to the master raw data server (“MRDS”) 5082. Inembodiments, information in the multimedia probe (“MMP”) and probecontrol, sequence and analytical (“PCSA”) information store 5040 mayalso be downloaded from the MRDS 5082 down to the DAQ instrument 5002.Further details of the MRDS 5082 are shown in FIG. 32 includingembodiments where data may be transferred to the MRDS 5082 from the DAQinstrument 5002 via a wired or wireless network, or through connectionto one or more portable media, drive, other network connections, or thelike. In embodiments, the DAQ instrument 5002 may be configured to beportable and may be carried on one or more predetermined routes toassess predefined points of measurement. In these many examples, theoperating system that may be included in the MRDS 5082 may be Windows™,Linux™ or MacOS™ operating systems, or other similar operating systems.Further, in these arrangements, the operating system, modules for theoperating system, and other needed libraries, data storage, and the likemay be accessible wholly or partially through access to the cloudnetwork facility 5080. In embodiments, the MRDS 5082 may reside directlyon the DAQ instrument 5002, especially in on-line system examples. Inembodiments, the DAQ instrument 5002 may be linked on an intra-networkin a facility but may otherwise be behind a firewall. In furtherexamples, the DAQ instrument 5002 may be linked to the cloud networkfacility 5080. In the various embodiments, one of the computers ormobile computing devices may be effectively designated the MRDS 5082 towhich all of the other computing devices may feed it data such as one ofthe MRDS 6104, as depicted in FIGS. 41 and 42. In the many exampleswhere the DAQ instrument 5002 may be deployed and configured to receivestream data in a swarm environment, one or more of the DAQ instruments5002 may be effectively designated the MRDS 5082 to which all of theother computing devices may feed it data. In the many examples where theDAQ instrument 5002 may be deployed and configured to receive streamdata in an environment where the methods and systems disclosed hereinare intelligently assigning, controlling, adjusting, and re-adjustingdata pools, computing resources, network bandwidth for local datacollection, and the like, one or more of the DAQ instruments 5002 may beeffectively designated the MRDS 5082 to which all of the other computingdevices may feed it data.

With further reference to FIG. 32, new raw streaming data, data thathave been through extract, process, and align processes (EP data), andthe like may be uploaded to one or more master raw data servers asneeded or as scaled in various environments. In embodiments, a masterraw data server (“MRDS”) 5700 may connect to and receive data from othermaster raw data servers such as the MRDS 5082. The MRDS 5700 may includea data distribution manager module 5702. In embodiments, the new rawstreaming data may be stored in the new stream data repository 5704. Inmany instances, like raw data streams stored on the DAQ instrument 5002,the new stream data repository 5704 and new extract and process datarepository 5708 may be similarly configured as a temporary storage area.

In embodiments, the MRDS 5700 may include a stream data analyzer modulewith an extract and process alignment module 5710. The analyzer module5710 may be shown to be a more robust data analyzer and extractor thanmay be typically found on portable streaming DAQ instruments although itmay be deployed on the DAQ instrument 5002 as well. In embodiments, theanalyzer module 5710 takes streaming data and instantiates it at aspecific sampling rate and resolution similar to the local data controlmodule 5062 on the DAQ instrument 5002. The specific sampling rate andresolution of the analyzer module 5710 may be based on either user input5712 or automated extractions from a multimedia probe (“MMP”) and theprobe control, sequence and analytical (“PCSA”) information store 5714and/or an identification mapping table 5718, which may require the userinput 5712 if there is incomplete information regarding various forms oflegacy data similar to as was detailed with the DAQ instrument 5002. Inembodiments, legacy data may be processed with the analyzer module 5710and may be stored in one or more temporary holding areas such as a newlegacy data repository 5720. One or more temporary areas may beconfigured to hold data until it is copied to an archive and verified.The analyzer 5710 module may also facilitate in-depth analysis byproviding many varying types of signal processing tools including butnot limited to filtering, Fourier transforms, weighting, resampling,envelope demodulation, wavelets, two-channel analysis, and the like.From this analysis, many different types of plots and mini-reports maybe generated from a reports and plots module 5724. In embodiments, datais sent to the processing, analysis, reports, and archiving (“PARA”)server 5730 upon user initiation or in an automated fashion especiallyfor on-line systems.

In embodiments, a PARA server 5750 may connect to and receive data fromother PARA servers such as the PARA server 5730. With reference to FIG.34, the PARA server 5730 may provide data to a supervisory module 5752on the PARA server 5750 that may be configured to provide at least oneof processing, analysis, reporting, archiving, supervisory, and similarfunctionalities. The supervisory module 5752 may also contain extract,process align functionality and the like. In embodiments, incomingstreaming data may first be stored in a raw data stream archive 5760after being properly validated. Based on the analytical requirementsderived from a multimedia probe (“MMP”) and probe control, sequence andanalytical (“PCSA”) information store 5762 as well as user settings,data may be extracted, analyzed, and stored in an extract and process(“EP”) raw data archive 5764. In embodiments, various reports from areports module 5768 are generated from the supervisory module 5752. Thevarious reports from the reports module 5768 include trend plots ofvarious smart bands, overalls along with statistical patterns, and thelike. In embodiments, the reports module 5768 may also be configured tocompare incoming data to historical data. By way of these examples, thereports module 5768 may search for and analyze adverse trends, suddenchanges, machinery defect patterns, and the like. In embodiments, thePARA server 5750 may include an expert analysis module 5770 from whichreports are generated and analysis may be conducted. Upon completion,archived data may be fed to a local master server (“LMS”) 5772 via aserver module 5774 that may connect to the local area network. Inembodiments, archived data may also be fed to the LMS 5772 via a clouddata management server (“CDMS”) 5778 through a server module for a cloudnetwork facility 5080. In embodiments, the supervisory module 5752 onthe PARA server 5750 may be configured to provide at least one ofprocessing, analysis, reporting, archiving, supervisory, and similarfunctionalities from which alarms may be generated, rated, stored,modified, reassigned, and the like with an alarm generator module 5782.

FIG. 34 depicts various embodiments that include a PARA server 5800 andits connection to LAN 5802. In embodiments, one or more DAQ instrumentssuch as the DAQ instrument 5002 may receive and process analog data fromone or more analog sensors 5710 that may be fed into the DAQ instrument5002. As discussed herein, the DAQ instrument 5002 may create a digitalstream of data based on the ingested analog data from the one or moreanalog sensors. The digital stream from the DAQ instrument 5002 may beuploaded to the MRDS 5082 and from there, it may be sent to the PARAserver 5800 where multiple terminals, such as terminal 5810 5812, 5814,may each interface with it or the MRDS 5082 and view the data and/oranalysis reports. In embodiments, the PARA server 5800 may communicatewith a network data server 5820 that may include a LMS 5822. In theseexamples, the LMS 5822 may be configured as an optional storage area forarchived data. The LMS 5822 may also be configured as an external driverthat may be connected to a PC or other computing device that may run theLMS 5822; or the LMS 5822 may be directly run by the PARA server 5800where the LMS 5822 may be configured to operate and coexist with thePARA server 5800. The LMS 5822 may connect with a raw data streamarchive 5824, an extract and process (“EP”) raw data archive 5828, and aMMP and probe control, sequence and analytical (“PCSA”) informationstore 5830. In embodiments, a CDMS 5832 may also connect to the LAN 5802and may also support the archiving of data.

In embodiments, portable connected devices 5850 such as a tablet 5852and a smart phone 5854 may connect the CDMS 5832 using web APIs 5860 and5862, respectively, as depicted in FIG. 35. The APIs 5860, 5862 may beconfigured to execute in a browser and may permit access via a cloudnetwork facility 5870 of all (or some of) the functions previouslydiscussed as accessible through the PARA Server 5800. In embodiments,computing devices of a user 5880 such as computing devices 5882, 5884,5888 may also access the cloud network facility 5870 via a browser orother connection in order to receive the same functionality. Inembodiments, thin-client apps which do not require any other devicedrivers and may be facilitated by web services supported by cloudservices 5890 and cloud data 5892. In many examples, the thin-clientapps may be developed and reconfigured using, for example, the visualhigh-level LabVIEW™ programming language with NXG™ Web-based virtualinterface subroutines. In embodiments, thin client apps may providehigh-level graphing functions such as those supported by LabVIEW™ tools.In embodiments, the LabVIEW™ tools may generate JSCRIPT™ code and JAVA™code that may be edited post-compilation. The NXG™ tools may generateWeb VI's that may not require any specialized driver and only someRESTful™ services which may be readily installed from any browser. Itwill be appreciated in light of the disclosure that because variousapplications may be run inside a browser, the applications may be run onany operating system, such as Windows™, Linux™, and Android™ operatingsystems especially for personal devices, mobile devices, portableconnected devices, and the like.

In embodiments, the CDMS 5832 is depicted in greater detail in FIG. 36.In embodiments, the CDMS 5832 may provide all of the data storage andservices that the PARA Server 5800 (FIG. 34) may provide. In contrast,all of the API's may be web API's which may run in a browser and allother apps may run on the PARA Server 5800 or the DAQ instrument 5002which may typically be Windows™, Linux™ or other similar operatingsystems. In embodiments, the CDMS 5832 includes at least one of orcombinations of the following functions: the CDMS 5832 may include acloud GUI 5900 that may be configured to provide access to all dataplots including trend, waveform, spectra, envelope, transfer function,logs of measurement events, analysis including expert, utilities, andthe like. In embodiments, the CDMS 5832 may include a cloud dataexchange 5902 configured to facilitate the transfer of data to and fromthe cloud network facility 5870. In embodiments, the CDMS 5832 mayinclude a cloud plots/trends module 5904 that may be configured to showall plots via web apps including trend, waveform, spectra, envelope,transfer function, and the like. In embodiments, the CDMS 5832 mayinclude a cloud reporter 5908 that may be configured to provide allanalysis reports, logs, expert analysis, trend plots, statisticalinformation, and the like. In embodiments, the CDMS 5832 may include acloud alarm module 5910. Alarms from the cloud alarm module 5910 may begenerated and may be sent to various devices 5920 via email, texts, orother messaging mechanisms. From the various modules, data may be storedin new data 5914. The various devices 5920 may include a terminal 5922,portable connected device 5924, or a tablet 5928. The alarms from thecloud alarm module are designed to be interactive so that the end usermay acknowledge alarms in order to avoid receiving redundant alarms andalso to see significant context-sensitive data from the alarm pointsthat may include spectra, waveform statistical info, and the like.

In embodiments, a relational database server (“RDS”) 5930 may be used toaccess all of the information from a MMP and PCSA information store5932. As with the PARA server 5800 (FIG. 36), information from theinformation store 5932 may be used with an EP and align module 5934, adata exchange 5938 and the expert system 5940. In embodiments, a rawdata stream archive 5942 and extract and process raw data archive 5944may also be used by the EP align 5934, the data exchange 5938 and theexpert system 5940 as with the PARA server 5800. In embodiments, newstream raw data 5950, new extract and process raw data 5952, and newdata 5954 (essentially all other raw data such as overalls, smart bands,stats, and data from the information store 5932) are directed by theCDMS 5832.

In embodiments, the streaming data may be linked with the RDS 5930 andthe MMP and PCSA information store 5932 using a technical datamanagement streaming (“TDMS”) file format. In embodiments, theinformation store 5932 may include tables for recording at leastportions of all measurement events. By way of these examples, ameasurement event may be any single data capture, a stream, a snapshot,an averaged level, or an overall level. Each of the measurement eventsin addition to point identification information may also have a date andtime stamp. In embodiments, a link may be made between the streamingdata, the measurement event, and the tables in the information store5932 using the TDMS format. By way of these examples, the link may becreated by storing unique measurement point identification codes with afile structure having the TDMS format by including and assigning TDMSproperties. In embodiments, a file with the TDMS format may allow forthree levels of hierarchy. By way of these examples, the three levels ofhierarchy may be root, group, and channel. It will be appreciated inlight of the disclosure that the Mimosa™ database schema may be, intheory, unlimited. With that said, there are advantages to limited TDMShierarchies. In the many examples, the following properties may beproposed for adding to the TDMS Stream structure while using a MimosaCompatible database schema.

Root Level: Global ID 1: Text String (This could be a unique ID obtainedfrom the web.); Global ID 2: Text String (This could be an additional IDobtained from the web.); Company Name: Text String; Company ID: TextString; Company Segment ID: 4-byte Integer; Company Segment ID: 4-byteInteger; Site Name: Text String; Site Segment ID: 4-byte Integer; SiteAsset ID: 4-byte Integer; Route Name: Text String; Version Number: TextString

Group Level: Section 1 Name: Text String; Section 1 Segment ID: 4-byteInteger; Section 1 Asset ID: 4-byte Integer; Section 2 Name: TextString; Section 2 Segment ID: 4-byte Integer; Section 2 Asset ID: 4-byteInteger; Machine Name: Text String; Machine Segment ID: 4-byte Integer;Machine Asset ID: 4-byte Integer; Equipment Name: Text String; EquipmentSegment ID: 4-byte Integer; Equipment Asset ID: 4-byte Integer; ShaftName: Text String; Shaft Segment ID: 4-byte Integer; Shaft Asset ID:4-byte Integer; Bearing Name: Text String; Bearing Segment ID: 4-byteInteger; Bearing Asset ID: 4-byte Integer; Probe Name: Text String;Probe Segment ID: 4-byte Integer; Probe Asset ID: 4-byte Integer

Channel Level: Channel #: 4-byte Integer; Direction: 4-byte Integer (incertain examples may be text); Data Type: 4-byte Integer; Reserved Name1: Text String; Reserved Segment ID 1: 4-byte Integer; Reserved Name 2:Text String; Reserved Segment ID 2: 4-byte Integer; Reserved Name 3:Text String; Reserved Segment ID 3: 4-byte Integer

In embodiments, the file with the TDMS format may automatically useproperty or asset information and may make an index file out of thespecific property and asset information to facilitate database searches,may offer a compromise for storing voluminous streams of data because itmay be optimized for storing binary streams of data but may also includesome minimal database structure making many standard SQL operationsfeasible, but the TDMS format and functionality discussed herein may notbe as efficient as a full-fledged SQL relational database. The TDMSformat, however, may take advantage of both worlds in that it maybalance between the class or format of writing and storing large streamsof binary data efficiently and the class or format of a fully relationaldatabase, which facilitates searching, sorting and data retrieval. Inembodiments, an optimum solution may be found in that metadata requiredfor analytical purposes and extracting prescribed lists with panelconditions for stream collection may be stored in the RDS 5930 byestablishing a link between the two database methodologies. By way ofthese examples, relatively large analog data streams may be storedpredominantly as binary storage in the raw data stream archive 5942 forrapid stream loading but with inherent relational SQL type hooks,formats, conventions, or the like. The files with the TDMS format mayalso be configured to incorporate DIAdem™ reporting capability ofLabVIEW™ software in order to provide a further mechanism toconveniently and rapidly facilitate accessing the analog or thestreaming data.

The methods and systems disclosed herein may include, connect to, or beintegrated with a virtual data acquisition instrument and in the manyembodiments, FIG. 37 shows methods and systems that include a virtualstreaming DAQ instrument 6000 also known as a virtual DAQ instrument, aVRDS, or a VSDAQ. In contrast to the DAQ instrument 5002 (FIG. 22), thevirtual DAQ instrument 6000 may be configured so to only include onenative application. In the many examples, the one permitted and onenative application may be the DAQ driver module 6002 that may manage allcommunications with the DAQ Device 6004 which may include streamingcapabilities. In embodiments, other applications, if any, may beconfigured as thin client web applications such as RESTful™ webservices. The one native application, or other applications or services,may be accessible through the DAQ Web API 6010. The DAQ Web API 6010 mayrun in or be accessible through various web browsers.

In embodiments, storage of streaming data, as well as the extraction andprocessing of streaming data into extract and process data, may behandled primarily by the DAQ driver services 6012 under the direction ofthe DAQ Web API 6010. In embodiments, the output from sensors of varioustypes including vibration, temperature, pressure, ultrasound and so onmay be fed into the instrument inputs of the DAQ device 6004. Inembodiments, the signals from the output sensors may be signalconditioned with respect to scaling and filtering and digitized with ananalog to a digital converter. In embodiments, the signals from theoutput sensors may be signals from all relevant channels simultaneouslysampled at a rate sufficient to perform the maximum desired frequencyanalysis. In embodiments, the signals from the output sensors may besampled for a relatively long time, gap-free, as one continuous streamso as to enable a wide array of further post-processing at lowersampling rates with sufficient samples. In further examples, streamingfrequency may be adjusted (and readjusted) to record streaming data atnon-evenly spaced recording. For temperature data, pressure data, andother similar data that may be relatively slow, varying delta timesbetween samples may further improve quality of the data. By way of theabove examples, data may be streamed from a collection of points andthen the next set of data may be collected from additional pointsaccording to a prescribed sequence, route, path, or the like. In themany examples, the portable sensors may be moved to the next locationaccording to the prescribed sequence but not necessarily all of them assome may be used for reference phase or otherwise. In further examples,a multiplexer 6020 may be used to switch to the next collection ofpoints or a mixture of the two methods may be combined.

In embodiments, the sequence and panel conditions that may be used togovern the data collection process using the virtual DAQ instrument 6000may be obtained from the MMP PCSA information store 6022. The MMP PCSAinformation store 6022 may include such items as the hierarchicalstructural relationships of the machine, i.e., a machine contains piecesof equipment in which each piece of equipment contains shafts and eachshaft is associated with bearings, which may be monitored by specifictypes of transducers or probes according to a specific prescribedsequence (routes, path, etc.) with specific panel conditions. By way ofthese examples, the panel conditions may include hardware specificswitch settings or other collection parameters such as sampling rate,AC/DC coupling, voltage range and gain, integration, high and low passfiltering, anti-aliasing filtering, ICP™ transducers and otherintegrated-circuit piezoelectric transducers, 4-20 mA loop sensors, andthe like. The information store 6022 includes other information that maybe stored in what would be machinery specific features that would beimportant for proper analysis including the number of gear teeth for agear, the number of blades in a pump impeller, the number of motor rotorbars, bearing specific parameters necessary for calculating bearingfrequencies, 1× rotating speed (RPMs) of all rotating elements, and thelike.

Upon direction of the DAQ Web API 6010 software, digitized waveforms maybe uploaded using the DAQ driver services 6012 of the virtual DAQinstrument 6000. In embodiments, data may then be fed into an RLN dataand control server 6030 that may store the stream data into a networkstream data repository 6032. Unlike the DAQ instrument 5002, the server6030 may run from within the DAQ driver module 6002. It will beappreciated in light of the disclosure that a separate application mayrequire drivers for running in the native operating system and for thisinstrument only the instrument driver may run natively. In manyexamples, all other applications may be configured to be browser based.As such, a relevant network variable may be very similar to a LabVIEW™shared or network stream variable which may be designed to be accessedover one or more networks or via web applications.

In embodiments, the DAQ web API 6010 may also direct the local datacontrol application 6034 to extract and process the recently obtainedstreaming data and, in turn, convert it to the same or lower samplingrates of sufficient length to provide the desired resolution. This datamay be converted to spectra, then averaged and processed in a variety ofways and stored as EP data, such as on an EP data repository 6040. TheEP data repository 6040 may, in certain embodiments, only be meant fortemporary storage. It will be appreciated in light of the disclosurethat legacy data may require its own sampling rates and resolution andoften this sampling rate may not be integer proportional to the acquiredsampling rate especially for order-sampled data whose sampling frequencyis related directly to an external frequency. The external frequency maytypically be the running speed of the machine or its internalcomponentry, rather than the more-standard sampling rates produced bythe internal crystals, clock functions, and the like of the (e.g.,values of Fmax of 100, 200, 500, 1K, 2K, 5K, 10K, 20K and so on) of theDAQ instrument 5002, 6000. In embodiments, the EP align component of thelocal data control application 6034 is able to fractionally adjust thesampling rate to the non-integer ratio rates that may be more applicableto legacy data sets and therefore drive compatibility with legacysystems. In embodiments, the fractional rates may be converted tointeger ratio rates more readily because the length of the data to beprocessed (or at least that portion of the greater stream of data) isadjustable because of the depth and content of the original acquiredstreaming data by the DAQ instrument 5002, 6000. It will be appreciatedin light of the disclosure that if the data was not streamed and juststored as traditional snap-shots of spectra with the standard values ofFmax, it may very well be impossible to retroactively and accuratelyconvert the acquired data to the order-sampled data. In embodiments, thestream data may be converted, especially for legacy data purposes, tothe proper sampling rate and resolution as described and stored in theEP legacy data repository 6042. To support legacy data identificationscenarios, a user input 6044 may be included if there is no automatedprocess for identification translation. In embodiments, one suchautomated process for identification translation may include importationof data from a legacy system that may contain a fully standardizedformat such as the Mimosa™ format and sufficient identificationinformation to complete an ID Mapping Table 6048. In further examples,the end user, a legacy data vendor, a legacy data storage facility, orthe like may be able to supply enough info to complete (or sufficientlycomplete) relevant portions of the ID Mapping Table 6048 to provide, inturn, the database schema for the raw data of the legacy system so itmay be readily ingested, saved, and used for analytics in the currentsystems disclosed herein.

FIG. 38 depicts further embodiments and details of the virtual DAQInstrument 6000. In these examples, the DAQ Web API 6010 may control thedata collection process as well as its sequence. The DAQ Web API 6010may provide the capability for editing this process, viewing plots ofthe data, controlling the processing of that data and viewing the outputin all its myriad forms, analyzing the data, including the expertanalysis, communicating with external devices via the DAQ driver module6002, as well as communicating with and transferring both streaming dataand EP data to one or more cloud network facilities 5080 wheneverpossible. In embodiments, the virtual DAQ instrument itself and the DAQWeb API 6010 may run independently of access to cloud network facilities5080 when local demands may require or simply as a result of there beingno outside connectivity such use throughout a proprietary industrialsetting that prevents such signals. In embodiments, the DAQ Web API 6010may also govern the movement of data, its filtering, as well as manyother housekeeping functions.

The virtual DAQ Instrument 6000 may also include an expert analysismodule 6052. In embodiments, the expert analysis module 6052 may be aweb application or other suitable module that may generate reports 6054that may use machine or measurement point specific information from theMMP PCSA information store 6022 to analyze stream data 6058 using thestream data analyzer module 6050. In embodiments, supervisory control ofthe module 6052 may be provided by the DAQ Web API 6010. In embodiments,the expert analysis may also be supplied (or supplemented) via theexpert system module 5940 that may be resident on one or more cloudnetwork facilities that are accessible via the CDMS 5832. In manyexamples, expert analysis via the cloud may be preferred over localsystems such as expert analysis module 6052 for various reasons, such asthe availability and use of the most up-to-date software version, moreprocessing capability, a bigger volume of historical data to referenceand the like. It will be appreciated in light of the disclosure that itmay be important to offer expert analysis when an internet connectioncannot be established so as to provide a redundancy, when needed, forseamless and time efficient operation. In embodiments, this redundancymay be extended to all of the discussed modular software applicationsand databases where applicable so each module discussed herein may beconfigured to provide redundancy to continue operation in the absence ofan internet connection.

FIG. 39 depicts further embodiments and details of many virtual DAQinstruments existing in an online system and connecting through networkendpoints through a central DAQ instrument to one or more cloud networkfacilities. In embodiments, a master DAQ instrument with networkendpoint 6060 is provided along with additional DAQ instruments such asa DAQ instrument with network endpoint 6062, a DAQ instrument withnetwork endpoint 6064, and a DAQ instrument with network endpoint 6068.The master DAQ instrument with network endpoint 6060 may connect withthe other DAQ instruments with network endpoints 6062, 6064, 6068 overLAN 6070. It will be appreciated that each of the instruments 6060,6062, 6064, 6068 may include personal computer, a connected device, orthe like that include Windows™, Linux™, or other suitable operatingsystems to facilitate ease of connection of devices utilizing many wiredand wireless network options such as Ethernet, wireless 802.11g, 900 MHzwireless (e.g., for better penetration of walls, enclosures and otherstructural barriers commonly encountered in an industrial setting), aswell as a myriad of other things permitted by the use of off-the-shelfcommunication hardware when needed.

FIG. 40 depicts further embodiments and details of many functionalcomponents of an endpoint that may be used in the various settings,environments, and network connectivity settings. The endpoint includesendpoint hardware modules 6080. In embodiments, the endpoint hardwaremodules 6080 may include one or more multiplexers 6082, a DAQ instrument6084, as well as a computer 6088, computing device, PC, or the like thatmay include the multiplexers, DAQ instruments, and computers, connecteddevices and the like, as disclosed herein. The endpoint software modules6090 include a data collector application (DCA) 6092 and a raw dataserver (RDS) 6094. In embodiments, DCA 6092 may be similar to the DAQAPI 5052 (FIG. 22) and may be configured to be responsible for obtainingstream data from the DAQ device 6084 and storing it locally according toa prescribed sequence or upon user directives. In the many examples, theprescribed sequence or user directives may be a LabVIEW™ software appthat may control and read data from the DAQ instruments. For cloud basedonline systems, the stored data in many embodiments may be networkaccessible. In many examples, LabVIEW™ tools may be used to accomplishthis with a shared variable or network stream (or subsets of sharedvariables). Shared variables and the affiliated network streams may benetwork objects that may be optimized for sharing data over the network.In many embodiments, the DCA 6092 may be configured with a graphic userinterface that may be configured to collect data as efficiently and fastas possible and push it to the shared variable and its affiliatednetwork stream. In embodiments, the endpoint raw data server 6094 may beconfigured to read raw data from the single-process shared variable andmay place it with a master network stream. In embodiments, a raw streamof data from portable systems may be stored locally and temporarilyuntil the raw stream of data is pushed to the MRDS 5082 (FIG. 22). Itwill be appreciated in light of the disclosure that on-line systeminstruments on a network can be termed endpoints whether local or remoteor associated with a local area network or a wide area network. Forportable data collector applications that may or may not be wirelesslyconnected to one or more cloud network facilities, the endpoint term maybe omitted as described so as to detail an instrument that may notrequire network connectivity.

FIG. 41 depicts further embodiments and details of multiple endpointswith their respective software blocks with at least one of the devicesconfigured as master blocks. Each of the blocks may include a datacollector application (“DCA”) 7000 and a raw data server (“RDS”) 7002.In embodiments, each of the blocks may also include a master raw dataserver module (“MRDS”) 7004, a master data collection and analysismodule (“MDCA”) 7008, and a supervisory and control interface module(“SCI”) 7010. The MRDS 7004 may be configured to read network streamdata (at a minimum) from the other endpoints and may forward it up toone or more cloud network facilities via the CDMS 5832 including thecloud services 5890 and the cloud data 5892. In embodiments, the CDMS5832 may be configured to store the data and to provide web, data, andprocessing services. In these examples, this may be implemented with aLabVIEW™ application that may be configured to read data from thenetwork streams or share variables from all of the local endpoints,write them to the local host PC, local computing device, connecteddevice, or the like, as both a network stream and file with TDMS™formatting. In embodiments, the CDMS 5832 may also be configured to thenpost this data to the appropriate buckets using the LabVIEW or similarsoftware that may be supported by S3™ web service from the Amazon WebServices (“AWS™”) on the Amazon™ web server, or the like and mayeffectively serve as a back-end server. In the many examples, differentcriteria may be enabled or may be set up for when to post data, createor adjust schedules, create or adjust event triggering including a newdata event, create a buffer full message, create or more alarmsmessages, and the like.

In embodiments, the MDCA 7008 may be configured to provide automated aswell as user-directed analyses of the raw data that may include trackingand annotating specific occurrence and in doing so, noting where reportsmay be generated and alarms may be noted. In embodiments, the SCI 7010may be an application configured to provide remote control of the systemfrom the cloud as well as the ability to generate status and alarms. Inembodiments, the SCI 7010 may be configured to connect to, interfacewith, or be integrated into a supervisory control and data acquisition(“SCADA”) control system. In embodiments, the SCI 7010 may be configuredas a LabVIEW™ application that may provide remote control and statusalerts that may be provided to any remote device that may connect to oneor more of the cloud network facilities 5870.

In embodiments, the equipment that is being monitored may include RFIDtags that may provide vital machinery analysis background information.The RFID tags may be associated with the entire machine or associatedwith the individual componentry and may be substituted when certainparts of the machine are replaced, repaired, or rebuilt. The RFID tagsmay provide permanent information relevant to the lifetime of the unitor may also be re-flashed to update with at least a portion of newinformation. In many embodiments, the DAQ instruments 5002 disclosedherein may interrogate the one or more RFID chips to learn of themachine, its componentry, its service history, and the hierarchicalstructure of how everything is connected including drive diagrams, wirediagrams, and hydraulic layouts. In embodiments, some of the informationthat may be retrieved from the RFID tags includes manufacturer,machinery type, model, serial number, model number, manufacturing date,installation date, lots numbers, and the like. By way of these examples,machinery type may include the use of a Mimosa™ format table includinginformation about one or more of the following motors, gearboxes, fans,and compressors. The machinery type may also include the number ofbearings, their type, their positioning, and their identificationnumbers. The information relevant to one or more fans includes fan type,number of blades, number of vanes, and number of belts. It will beappreciated in light of the disclosure that other machines and theircomponentry may be similarly arranged hierarchically with relevantinformation all of which may be available through interrogation of oneor more RFID chips associated with the one or more machines.

In embodiments, data collection in an industrial environment may includerouting analog signals from a plurality of sources, such as analogsensors, to a plurality of analog signal processing circuits. Routing ofanalog signals may be accomplished by an analog crosspoint switch thatmay route any of a plurality of analog input signals to any of aplurality of outputs, such as to analog and/or digital outputs. Routingof inputs to outputs in an analog signal crosspoint switch in anindustrial environment may be configurable, such as by an electronicsignal to which a switch portion of the analog crosspoint switch isresponsive.

In embodiments, the analog crosspoint switch may receive analog signalsfrom a plurality of analog signal sources in the industrial environment.Analog signal sources may include sensors that produce an analog signal.Sensors that produce an analog signal that may be switched by the analogcrosspoint switch may include sensors that detect a condition andconvert it to an analog signal that may be representative of thecondition, such as converting a condition to a corresponding voltage.Exemplary conditions that may be represented by a variable voltage mayinclude temperature, friction, sound, light, torque,revolutions-per-minute, mechanical resistance, pressure, flow rate, andthe like, including any of the conditions represented by inputs sourcesand sensors disclosed throughout this disclosure and the documentsincorporated herein by reference. Other forms of analog signal mayinclude electrical signals, such as variable voltage, variable current,variable resistance, and the like.

In embodiments, the analog crosspoint switch may preserve one or moreaspects of an analog signal being input to it in an industrialenvironment. Analog circuits integrated into the switch may providebuffered outputs. The analog circuits of the analog crosspoint switchmay follow an input signal, such as an input voltage to produce abuffered representation on an output. This may alternatively beaccomplished by relays (mechanical, solid state, and the like) thatallow an analog voltage or current present on an input to propagate to aselected output of the analog switch.

In embodiments, an analog crosspoint switch in an industrial environmentmay be configured to switch any of a plurality of analog inputs to anyof a plurality of analog outputs. An example embodiment includes a MIMO,multiplexed configuration. An analog crosspoint switch may bedynamically configurable so that changes to the configuration causes achange in the mapping of inputs to outputs. A configuration change mayapply to one or more mappings so that a change in mapping may result inone or more of the outputs being mapped to different input than beforethe configuration change.

In embodiments, the analog crosspoint switch may have more inputs thanoutputs, so that only a subset of inputs can be routed to outputsconcurrently. In other embodiments, the analog crosspoint switch mayhave more outputs than inputs, so that either a single input may be madeavailable currently on multiple outputs, or at least one output may notbe mapped to any input.

In embodiments, an analog crosspoint switch in an industrial environmentmay be configured to switch any of a plurality of analog inputs to anyof a plurality of digital outputs. To accomplish conversion from analoginputs to digital outputs, an analog-to-digital converter circuit may beconfigured on each input, each output, or at intermediate points betweenthe input(s) and output(s) of the analog crosspoint switch. Benefits ofincluding digitization of analog signals in an analog crosspoint switchthat may be located close to analog signal sources may include reducingsignal transport costs and complexity that digital signal communicationhas over analog, reducing energy consumption, facilitating detection andregulation of aberrant conditions before they propagate throughout anindustrial environment, and the like. Capturing analog signals close totheir source may also facilitate improved signal routing management thatis more tolerant of real world effects such as requiring that multiplesignals be routed simultaneously. In this example, a portion of thesignals can be captured (and stored) locally while another portion canbe transferred through the data collection network. Once the datacollection network has available bandwidth, the locally stored signalscan be delivered, such as with a time stamp indicating the time at whichthe data was collected. This technique may be useful for applicationsthat have concurrent demand for data collection channels that exceed thenumber of channels available. Sampling control may also be based on anindication of data worth sampling. As an example, a signal source, suchas a sensor in an industrial environment may provide a data valid signalthat transmits an indication of when data from the sensor is available.

In embodiments, mapping inputs of the analog crosspoint switch tooutputs may be based on a signal route plan for a portion of theindustrial environment that may be presented to the crosspoint switch.The signal route plan may be used in a method of data collection in theindustrial environment that may include routing a plurality of analogsignals along a plurality of analog signal paths. The method may includeconnecting the plurality of analog signals individually to inputs of theanalog crosspoint switch that may be configured with a route plan. Thecrosspoint switch may, responsively to the configured route plan, routea portion of the plurality of analog signals to a portion of theplurality of analog signal paths.

In embodiments, the analog crosspoint switch may include at least onehigh current output drive circuit that may be suitable for routing theanalog signal along a path that requires high current. In embodiments,the analog crosspoint switch may include at least one voltage-limitedinput that may facilitate protecting the analog crosspoint switch fromdamage due to excessive analog input signal voltage. In embodiments, theanalog crosspoint switch may include at least one current limited inputthat may facilitate protecting the analog crosspoint switch from damagedue to excessive analog input current. The analog crosspoint switch maycomprise a plurality of interconnected relays that may facilitaterouting the input(s) to the output(s) with little or no substantivesignal loss.

In embodiments, an analog crosspoint switch may include processingfunctionality, such as signal processing and the like (e.g., aprogrammed processor, special purpose processor, a digital signalprocessor, and the like) that may detect one or more analog input signalconditions. In response to such detection, one or more actions may beperformed, such as setting an alarm, sending an alarm signal to anotherdevice in the industrial environment, changing the crosspoint switchconfiguration, disabling one or more outputs, powering on or off aportion of the switch, changing a state of an output, such as a generalpurpose digital or analog output, and the like. In embodiments, theswitch may be configured to process inputs for producing a signal on oneor more of the outputs. The inputs to use, processing algorithm for theinputs, condition for producing the signal, output to use, and the likemay be configured in a data collection template.

In embodiments, an analog crosspoint switch may comprise greater than 32inputs and greater than 32 outputs. A plurality of analog crosspointswitches may be configured so that even though each switch offers fewerthan 32 inputs and 32 outputs it may be configured to facilitateswitching any of 32 inputs to any of 32 outputs spread across theplurality of crosspoint switches.

In embodiments, an analog crosspoint switch suitable for use in anindustrial environment may comprise four or fewer inputs and four orfewer outputs. Each output may be configurable to produce an analogoutput that corresponds to the mapped analog input or it may beconfigured to produce a digital representation of the correspondingmapped input.

In embodiments, an analog crosspoint switch for use in an industrialenvironment may be configured with circuits that facilitate replicatingat least a portion of attributes of the input signal, such as current,voltage range, offset, frequency, duty cycle, ramp rate, and the likewhile buffering (e.g., isolating) the input signal from the outputsignal. Alternatively, an analog crosspoint switch may be configuredwith unbuffered inputs/outputs, thereby effectively producing abi-directional based crosspoint switch).

In embodiments, an analog crosspoint switch for use in an industrialenvironment may include protected inputs that may be protected fromdamaging conditions, such as through use of signal conditioningcircuits. Protected inputs may prevent damage to the switch and todownstream devices to which the switch outputs connect. As an example,inputs to such an analog crosspoint switch may include voltage clippingcircuits that prevent a voltage of an input signal from exceeding aninput protection threshold. An active voltage adjustment circuit mayscale an input signal by reducing it uniformly so that a maximum voltagepresent on the input does not exceed a safe threshold value. As anotherexample, inputs to such an analog crosspoint switch may include currentshunting circuits that cause current beyond a maximum input protectioncurrent threshold to be diverted through protection circuits rather thanenter the switch. Analog switch inputs may be protected fromelectrostatic discharge and/or lightning strikes. Other signalconditioning functions that may be applied to inputs to an analogcrosspoint switch may include voltage scaling circuitry that attempts tofacilitate distinguishing between valid input signals and low voltagenoise that may be present on the input. However, in embodiments, inputsto the analog crosspoint switch may be unbuffered and/or unprotected tomake the least impact on the signal. Signals such as alarm signals, orsignals that cannot readily tolerate protection schemes, such as thoseschemes described above herein may be connected to unbuffered inputs ofthe analog crosspoint switch.

In embodiments, an analog crosspoint switch may be configured withcircuitry, logic, and/or processing elements that may facilitate inputsignal alarm monitoring. Such an analog crosspoint switch may detectinputs meeting alarm conditions and in response thereto, switch inputs,switch mapping of inputs to outputs, disable inputs, disable outputs,issue an alarm signal, activate/deactivate a general-purpose output, orthe like.

In embodiments, a system for collecting data in an industrialenvironment may include an analog crosspoint switch that may be adaptedto selectively power up or down portions of the analog crosspoint switchor circuitry associated with the analog crosspoint switch, such as inputprotection devices, input conditioning devices, switch control devicesand the like. Portions of the analog crosspoint switch that may bepowered on/off may include outputs, inputs, sections of the switch andthe like. In an example, an analog crosspoint switch may include amodular structure that may separate portions of the switch intoindependently powered sections. Based on conditions, such as an inputsignal meeting a criterion or a configuration value being presented tothe analog crosspoint switch, one or more modular sections may bepowered on/off.

In embodiments, a system for collecting data in an industrialenvironment may include an analog crosspoint switch that may be adaptedto perform signal processing including, without limitation, providing avoltage reference for detecting an input crossing the voltage reference(e.g., zero volts for detecting zero-crossing signals), a phase-lockloop to facilitate capturing slow frequency signals (e.g., low-speedrevolution-per-minute signals and detecting their corresponding phase),deriving input signal phase relative to other inputs, deriving inputsignal phase relative to a reference (e.g., a reference clock), derivinginput signal phase relative to detected alarm input conditions and thelike. Other signal processing functions of such an analog crosspointswitch may include oversampling of inputs for delta-sigma A/D, toproduce lower sampling rate outputs, to minimize AA filter requirementsand the like. Such an analog crosspoint switch may support long blocksampling at a constant sampling rate even as inputs are switched, whichmay facilitate input signal rate independence and reduce complexity ofsampling scheme(s). A constant sampling rate may be selected from aplurality of rates that may be produced by a circuit, such as a clockdivider circuit that may make available a plurality of components of areference clock.

In embodiments, a system for collecting data in an industrialenvironment may include an analog crosspoint switch that may be adaptedto support implementing data collection/data routing templates in theindustrial environment. The analog crosspoint switch may implement adata collection/data routing template based on conditions in theindustrial environment that it may detect or derive, such as an inputsignal meeting one or more criteria (e.g., transition of a signal from afirst condition to a second, lack of transition of an input signalwithin a predefined time interface (e.g., inactive input) and the like).

In embodiments, a system for collecting data in an industrialenvironment may include an analog crosspoint switch that may be adaptedto be configured from a portion of a data collection template.Configuration may be done automatically (without needing humanintervention to perform a configuration action or change inconfiguration), such as based on a time parameter in the template andthe like. Configuration may be done remotely, e.g., by sending a signalfrom a remote location that is detectable by a switch configurationfeature of the analog crosspoint switch. Configuration may be donedynamically, such as based on a condition that is detectable by aconfiguration feature of the analog crosspoint switch (e.g., a timer, aninput condition, an output condition, and the like). In embodiments,information for configuring an analog crosspoint switch may be providedin a stream, as a set of control lines, as a data file, as an indexeddata set, and the like. In embodiments, configuration information in adata collection template for the switch may include a list of each inputand a corresponding output, a list of each output function (active,inactive, analog, digital and the like), a condition for updating theconfiguration (e.g., an input signal meeting a condition, a triggersignal, a time (relative to another time/event/state, or absolute), aduration of the configuration, and the like. In embodiments,configuration of the switch may be input signal protocol aware so thatswitching from a first input to a second input for a given output mayoccur based on the protocol. In an example, a configuration change maybe initiated with the switch to switch from a first video signal to asecond video signal. The configuration circuitry may detect the protocolof the input signal and switch to the second video signal during asynchronization phase of the video signal, such as during horizontal orvertical refresh. In other examples, switching may occur when one ormore of the inputs are at zero volts. This may occur for a sinusoidalsignal that transitions from below zero volts to above zero volts.

In embodiments, a system for collecting data in an industrialenvironment may include an analog crosspoint switch that may be adaptedto provide digital outputs by converting analog signals input to theswitch into digital outputs. Converting may occur after switching theanalog inputs based on a data collection template and the like. Inembodiments, a portion of the switch outputs may be digital and aportion may be analog. Each output, or groups thereof, may beconfigurable as analog or digital, such as based on analog crosspointswitch output configuration information included in or derived from adata collection template. Circuitry in the analog crosspoint switch maysense an input signal voltage range and intelligently configure ananalog-to-digital conversion function accordingly. As an example, afirst input may have a voltage range of 12 volts and a second input mayhave a voltage range of 24 volts. Analog-to-digital converter circuitsfor these inputs may be adjusted so that the full range of the digitalvalue (e.g., 256 levels for an 8-bit signal) will map substantiallylinearly to 12 volts for the first input and 24 volts for the secondinput.

In embodiments, an analog crosspoint switch may automatically configureinput circuitry based on characteristics of a connected analog signal.Examples of circuitry configuration may include setting a maximumvoltage, a threshold based on a sensed maximum threshold, a voltagerange above and/or below a ground reference, an offset reference, andthe like. The analog crosspoint switch may also adapt inputs to supportvoltage signals, current signals, and the like. The analog crosspointswitch may detect a protocol of an input signal, such as a video signalprotocol, audio signal protocol, digital signal protocol, protocol basedon input signal frequency characteristics, and the like. Other aspectsof inputs of the analog crosspoint switch that may be adapted based onthe incoming signal may include a duration of sampling of the signal,and comparator or differential type signals, and the like.

In embodiments, an analog crosspoint switch may be configured withfunctionality to counteract input signal drift and/or leakage that mayoccur when an analog signal is passed through it over a long period oftime without changing value (e.g., a constant voltage). Techniques mayinclude voltage boost, current injection, periodic zero referencing(e.g., temporarily connecting the input to a reference signal, such asground, applying a high resistance pathway to the ground reference, andthe like).

In embodiments, a system for data collection in an industrialenvironment may include an analog crosspoint switch deployed in anassembly line comprising conveyers and/or lifters. A power rollerconveyor system includes many rollers that deliver product along a path.There may be many points along the path that may be monitored for properoperation of the rollers, load being placed on the rollers, accumulationof products, and the like. A power roller conveyor system may alsofacilitate moving product through longer distances and therefore mayhave a large number of products in transport at once. A system for datacollection in such an assembly environment may include sensors thatdetect a wide range of conditions as well as at a large number ofpositions along the transport path. As a product progresses down thepath, some sensors may be active and others, such as those that theproduct has passed maybe inactive. A data collection system may use ananalog crosspoint switch to select only those sensors that are currentlyor anticipated to be active by switching from inputs that connect toinactive sensors to those that connect to active sensors and therebyprovide the most useful sensor signals to data detection and/orcollection and/or processing facilities. In embodiments, the analogcrosspoint switch may be configured by a conveyor control system thatmonitors product activity and instructs the analog crosspoint switch todirect different inputs to specific outputs based on a control programor data collection template associated with the assembly environment.

In embodiments, a system for data collection in an industrialenvironment may include an analog crosspoint switch deployed in afactory comprising use of fans as industrial components. In embodiments,fans in a factory setting may provide a range of functions includingdrying, exhaust management, clean air flow and the like. In aninstallation of a large number of fans, monitoring fan rotational speed,torque, and the like may be beneficial to detect an early indication ofa potential problem with air flow being produced by the fans. However,concurrently monitoring each of these elements for a large number offans may be inefficient. Therefore, sensors, such as tachometers, torquemeters, and the like may be disposed at each fan and their analog outputsignal(s) may be provided to an analog crosspoint switch. With a limitednumber of outputs, or at least a limited number of systems that canprocess the sensor data, the analog crosspoint switch may be used toselect among the many sensors and pass along a subset of the availablesensor signals to data collection, monitoring, and processing systems.In an example, sensor signals from sensors disposed at a group of fansmay be selected to be switched onto crosspoint switch outputs. Uponsatisfactory collection and/or processing of the sensor signals for thisgroup of fans, the analog crosspoint switch may be reconfigured toswitch signals from another group of fans to be processed.

In embodiments, a system for data collection in an industrialenvironment may include an analog crosspoint switch deployed as anindustrial component in a turbine-based power system. Monitoring forvibration in turbine systems, such as hydro-power systems, has beendemonstrated to provide advantages in reduction in down time. However,with a large number of areas to monitor for vibration, particularly foron-line vibration monitoring, including relative shaft vibration,bearings absolute vibration, turbine cover vibration, thrust bearingaxial vibration, stator core vibrations, stator bar vibrations, statorend winding vibrations, and the like, it may be beneficial to selectamong this list over time, such as taking samples from sensors for eachof these types of vibration a few at a time. A data collection systemthat includes an analog crosspoint switch may provide this capability byconnecting each vibration sensor to separate inputs of the analogcrosspoint switch and configuring the switch to output a subset of itsinputs. A vibration data processing system, such as a computer, maydetermine which sensors to pass through the analog crosspoint switch andconfigure an algorithm to perform the vibration analysis accordingly. Asan example, sensors for capturing turbine cover vibration may beselected in the analog crosspoint switch to be passed on to a systemthat is configured with an algorithm to determine turbine covervibration from the sensor signals. Upon completion of determiningturbine cover vibration, the crosspoint switch may be configured to passalong thrust bearing axial vibration sensor signals and a correspondingvibration analysis algorithm may be applied to the data. In this way,each type of vibration may be analyzed by a single processing systemthat works cooperatively with an analog crosspoint switch to passspecific sensor signals for processing.

Referring to FIG. 44, an analog crosspoint switch for collecting data inan industrial environment is depicted. The analog crosspoint switch 7022may have a plurality of inputs 7024 that connect to sensors 7026 in theindustrial environment. The analog crosspoint switch 7022 may alsocomprise a plurality of outputs 7028 that connect to data collectioninfrastructure, such as analog-to-digital converters 7030, analogcomparators 7032, and the like. The analog crosspoint switch 7022 mayfacilitate connecting one or more inputs 7024 to one or more outputs7028 by interpreting a switch control value that may be provided to itby a controller 7034 and the like.

An example system for data collection in an industrial environmentcomprising includes analog signal sources that each connect to at leastone input of an analog crosspoint switch including a plurality of inputsand a plurality of outputs; where the analog crosspoint switch isconfigurable to switch a portion of the input signal sources to aplurality of the outputs.

2. In certain embodiments, the analog crosspoint switch further includesan analog-to-digital converter that converts a portion of analog signalsinput to the crosspoint switch into representative digital signals; aportion of the outputs including analog outputs and a portion of theoutputs comprises digital outputs; and/or where the analog crosspointswitch is adapted to detect one or more analog input signal conditions.Any one or more of the example embodiments include the analog inputsignal conditions including a voltage range of the signal, and where theanalog crosspoint switch responsively adjusts input circuitry to complywith detected voltage range.

An example system of data collection in an industrial environmentincludes a number of industrial sensors that produce analog signalsrepresentative of a condition of an industrial machine in theenvironment being sensed by the number of industrial sensors, acrosspoint switch that receives the analog signals and routes the analogsignals to separate analog outputs of the crosspoint switch based on asignal route plan presented to the crosspoint switch. In certainembodiments, the analog crosspoint switch further includes ananalog-to-digital converter that converts a portion of analog signalsinput to the crosspoint switch into representative digital signals;where a portion of the outputs include analog outputs and a portion ofthe outputs include digital outputs; where the analog crosspoint switchis adapted to detect one or more analog input signal conditions; wherethe one or more analog input signal conditions include a voltage rangeof the signal, and/or where the analog crosspoint switch responsivelyadjusts input circuitry to comply with detected voltage range.

An example method of data collection in an industrial environmentincludes routing a number of analog signals along a plurality of analogsignal paths by connecting the plurality of analog signals individuallyto inputs of an analog crosspoint switch, configuring the analogcrosspoint switch with data routing information from a data collectiontemplate for the industrial environment routing, and routing with theconfigured analog crosspoint switch a portion of the number of analogsignals to a portion the plurality of analog signal paths. In certainfurther embodiments, at least one output of the analog crosspoint switchincludes a high current driver circuit; at least one input of the analogcrosspoint switch includes a voltage limiting circuit; and/or at leastone input of the analog crosspoint switch includes a current limitingcircuit. In certain further embodiments, the analog crosspoint switchincludes a number of interconnected relays that facilitate connectingany of a number of inputs to any of a plurality of outputs; the analogcrosspoint switch further including an analog-to-digital converter thatconverts a portion of analog signals input to the crosspoint switch intoa representative digital signal; the analog crosspoint switch furtherincluding signal processing functionality to detect one or more analoginput signal conditions, and in response thereto, to perform an action(e.g., set an alarm, change switch configuration, disable one or moreoutputs, power off a portion of the switch, change a state of a generalpurpose (digital/analog) output, etc.); where a portion of the outputsare analog outputs and a portion of the outputs are digital outputs;where the analog crosspoint switch is adapted to detect one or moreanalog input signal conditions; where the analog crosspoint switch isadapted to take one or more actions in response to detecting the one ormore analog input signal conditions, the one more actions includingsetting an alarm, sending an alarm signal, changing a configuration ofthe analog crosspoint switch, disabling an output, powering off aportion of the analog crosspoint switch, powering on a portion of theanalog crosspoint switch, and/or controlling a general purpose output ofthe analog crosspoint switch.

An example system includes a power roller of a conveyor, including anyof the described operations of an analog crosspoint switch. Withoutlimitation, further example embodiments includes sensing conditions ofthe power roller by the sensors to determine a rate of rotation of thepower roller, a load being transported by the power roller, power beingconsumed by the power roller, and/or a rate of acceleration of the powerroller. An example system includes a fan in a factory setting, includingany of the described operations of an analog crosspoint switch. Withoutlimitation, certain further embodiments include sensors disposed tosense conditions of the fan, including a fan blade tip speed, torque,back pressure, RPMs, and/or a volume of air per unit time displaced bythe fan. An example system includes a turbine in a power generationenvironment, including any of the described operations of an analogcrosspoint switch. Without limitation, certain further embodimentsinclude a number of sensors disposed to sense conditions of the turbine,where the sensed conditions include a relative shaft vibration, anabsolute vibration of bearings, a turbine cover vibration, a thrustbearing axial vibration, vibrations of stators or stator cores,vibrations of stator bars, and/or vibrations of stator end windings.

In embodiments, methods and systems of data collection in an industrialenvironment may include a plurality of industrial condition sensing andacquisition modules that may include at least one programmable logiccomponent per module that may control a portion of the sensing andacquisition functionality of its module. The programmable logiccomponents on each of the modules may be interconnected by a dedicatedlogic bus that may include data and control channels. The dedicatedlogic bus may extend logically and/or physically to other programmablelogic components on other sensing and acquisition modules. Inembodiments, the programmable logic components may be programmed via thededicated interconnection bus, via a dedicated programming portion ofthe dedicated interconnection bus, via a program that is passed betweenprogrammable logic components, sensing and acquisition modules, or wholesystems. A programmable logic component for use in an industrialenvironment data sensing and acquisition system may be a ComplexProgrammable Logic Device, an Application-Specific Integrated Circuit,microcontrollers, and combinations thereof.

A programmable logic component in an industrial data collectionenvironment may perform control functions associated with datacollection. Control examples include power control of analog channels,sensors, analog receivers, analog switches, portions of logic modules(e.g., a logic board, system, and the like) on which the programmablelogic component is disposed, self-power-up/down, self-sleep/wake up, andthe like. Control functions, such as these and others, may be performedin coordination with control and operational functions of otherprogrammable logic components, such as other components on a single datacollection module and components on other such modules. Other functionsthat a programmable logic component may provide may include generationof a voltage reference, such as a precise voltage reference for inputsignal condition detection. A programmable logic component may generate,set, reset, adjust, calibrate, or otherwise determine the voltage of thereference, its tolerance, and the like. Other functions of aprogrammable logic component may include enabling a digital phase lockloop to facilitate tracking slowly transitioning input signals, andfurther to facilitate detecting the phase of such signals. Relativephase detection may also be implemented, including phase relative totrigger signals, other analog inputs, on-board references (e.g.,on-board timers), and the like. A programmable logic component may beprogrammed to perform input signal peak voltage detection and controlinput signal circuitry, such as to implement auto-scaling of the inputto an operating voltage range of the input. Other functions that may beprogrammed into a programmable logic component may include determiningan appropriate sampling frequency for sampling inputs independently oftheir operating frequencies. A programmable logic component may beprogrammed to detect a maximum frequency among a plurality of inputsignals and set a sampling frequency for each of the input signals thatis greater than the detected maximum frequency.

A programmable logic component may be programmed to configure andcontrol data routing components, such as multiplexers, crosspointswitches, analog-to-digital converters, and the like, to implement adata collection template for the industrial environment. A datacollection template may be included in a program for a programmablelogic component. Alternatively, an algorithm that interprets a datacollection template to configure and control data routing resources inthe industrial environment may be included in the program.

In embodiments, one or more programmable logic components in anindustrial environment may be programmed to perform smart-band signalanalysis and testing. Results of such analysis and testing may includetriggering smart band data collection actions, that may includereconfiguring one or more data routing resources in the industrialenvironment. A programmable logic component may be configured to performa portion of smart band analysis, such as collection and validation ofsignal activity from one or more sensors that may be local to theprogrammable logic component. Smart band signal analysis results from aplurality of programmable logic components may be further processed byother programmable logic components, servers, machine learning systems,and the like to determine compliance with a smart band.

In embodiments, one or more programmable logic components in anindustrial environment may be programmed to control data routingresources and sensors for outcomes, such as reducing power consumption(e.g., powering on/off resources as needed), implementing security inthe industrial environment by managing user authentication, and thelike. In embodiments, certain data routing resources, such asmultiplexers and the like, may be configured to support certain inputsignal types. A programmable logic component may configure the resourcesbased on the type of signals to be routed to the resources. Inembodiments, the programmable logic component may facilitatecoordination of sensor and data routing resource signal type matching byindicating to a configurable sensor a protocol or signal type to presentto the routing resource. A programmable logic component may facilitatedetecting a protocol of a signal being input to a data routing resource,such as an analog crosspoint switch and the like. Based on the detectedprotocol, the programmable logic component may configure routingresources to facilitate support and efficient processing of theprotocol. In an example, a programmable logic component configured datacollection module in an industrial environment may implement anintelligent sensor interface specification, such as IEEE 1451.2intelligent sensor interface specification.

In embodiments, distributing programmable logic components across aplurality of data sensing, collection, and/or routing modules in anindustrial environment may facilitate greater functionality and localinter-operational control. In an example, modules may performoperational functions independently based on a program installed in oneor more programmable logic components associated with each module. Twomodules may be constructed with substantially identical physicalcomponents, but may perform different functions in the industrialenvironment based on the program(s) loaded into programmable logiccomponent(s) on the modules. In this way, even if one module were toexperience a fault, or be powered down, other modules may continue toperform their functions due at least in part to each having its ownprogrammable logic component(s). In embodiments, configuring a pluralityof programmable logic components distributed across a plurality of datacollection modules in an industrial environment may facilitatescalability in terms of conditions in the environment that may besensed, the number of data routing options for routing sensed datathroughout the industrial environment, the types of conditions that maybe sensed, the computing capability in the environment, and the like.

In embodiments, a programmable logic controller-configured datacollection and routing system may facilitate validation of externalsystems for use as storage nodes, such as for a distributed ledger, andthe like. A programmable logic component may be programmed to performvalidation of a protocol for communicating with such an external system,such as an external storage node.

In embodiments, programming of programmable logic components, such asCPLDs and the like may be performed to accommodate a range of datasensing, collection and configuration differences. In embodiments,reprogramming may be performed on one or more components when addingand/or removing sensors, when changing sensor types, when changingsensor configurations or settings, when changing data storageconfigurations, when embedding data collection template(s) into deviceprograms, when adding and/or removing data collection modules (e.g.,scaling a system), when a lower cost device is used that may limitfunctionality or resources over a higher cost device, and the like. Aprogrammable logic component may be programmed to propagate programs forother programmable components via a dedicated programmable logic deviceprogramming channel, via a daisy chain programming architecture, via amesh of programmable logic components, via a hub-and-spoke architectureof interconnected components, via a ring configuration (e.g., using acommunication token, and the like).

Referring to FIG. 45, an exemplary embodiment of a system for datacollection in an industrial environment comprising distributed CPLDsinterconnected by a bus for control and/or programming thereof isdepicted. An exemplary data collection module 7200 may comprise one ormore CPLDs 7206 for controlling one or more data collection systemresources, such as sensors 7202 and the like. Other data collectionresources that a CPLD may control may include crosspoint switches,multiplexers, data converters, and the like. CPLDs on a module may beinterconnected by a bus, such as a dedicated logic bus 7204 that mayextend beyond a data collection module to CPLDs on other data collectionmodules. Data collection modules, such as module 7200 may be configuredin the environment, such as on an industrial machine 7208 (e.g., animpulse gas turbine) and/or 7210 (e.g., a co-generation system), and thelike. Control and/or configuration of the CPLDs may be handled by acontroller 7212 in the environment. Data collection and routingresources and interconnection (not shown) may also be configured withinand among data collection modules 7200 as well as between and amongindustrial machines 7208 and 7210, and/or with external systems, such asInternet portals, data analysis servers, and the like to facilitate datacollection, routing, storage, analysis, and the like.

An example system for data collection in an industrial environmentincludes a number of industrial condition sensing and acquisitionmodules, with a programmable logic component disposed on each of themodules, where the programmable logic component controls a portion ofthe sensing and acquisition functional of the corresponding module. Thesystem includes communication bus that is dedicated to interconnectingthe at least one programmable logic component disposed on at least oneof the plurality of modules, wherein the communication bus extends toother programmable logic components on other sensing and acquisitionmodules.

In certain further embodiments, a system includes the programmable logiccomponent programmed via the communication bus, the communication busincluding a portion dedicated to programming of the programmable logiccomponents, controlling a portion of the sensing and acquisitionfunctionality of a module by a power control function such as:controlling power of a sensor, a multiplexer, a portion of the module,and/or controlling a sleep mode of the programmable logic component;controlling a portion of the sensing and acquisition functionality of amodule by providing a voltage reference to a sensor and/or ananalog-to-digital converter disposed on the module, by detecting arelative the phase of at least two analog signals derived from at leasttwo sensors disposed on the module; by controlling sampling of dataprovided by at least one sensor disposed on the module; by detecting apeak voltage of a signal provided by a sensor disposed on the module;and/or by configuring at least one multiplexer disposed on the module byspecifying to the multiplexer a mapping of at least one input and oneoutput. In certain embodiments, the communication bus extends to otherprogrammable logic components on other condition sensing and/oracquisition modules. In certain embodiments, a module may be anindustrial environment condition sensing module. In certain embodiments,a module control program includes an algorithm for implementing anintelligent sensor interface communication protocol, such as anIEEE1451.2 compatible intelligent sensor interface communicationprotocol. In certain embodiments, a programmable logic componentincludes configuring the programmable logic component and/or the sensingor acquisition module to implement a smart band data collectiontemplate. Example and non-limiting programmable logic components includefield programmable gate arrays, complex programmable logic devices,and/or microcontrollers.

Exemplary deployment environments may include environments with triggersignal channel limitations, such as existing data collection systemsthat do not have separate trigger support for transporting an additionaltrigger signal to a module with sufficient computing sophistication toperform trigger detection. Another exemplary deployment may includesystems that require at least some autonomous control for performingdata collection.

In embodiments, a system for data collection in an industrialenvironment may include an analog switch that switches between a firstinput, such as a trigger input and a second input, such as a data inputbased on a condition of the first input. A trigger input may bemonitored by a portion of the analog switch to detect a change in thesignal, such as from a lower voltage to a higher voltage relative to areference or trigger threshold voltage. In embodiments, a device thatmay receive the switched signal from the analog switch may monitor thetrigger signal for a condition that indicates a condition for switchingfrom the trigger input to the data input. When a condition of thetrigger input is detected, the analog switch may be reconfigured, todirect the data input to the same output that was propagating thetrigger output.

In embodiments, a system for data collection in an industrialenvironment may include an analog switch that directs a first input toan output of the analog switch until such time as the output of theanalog switch indicates that a second input should be directed to theoutput of the analog switch. The output of the analog switch maypropagate a trigger signal to the output. In response to the triggersignal propagating through the switch transitioning from a firstcondition (e.g., a first voltage below a trigger threshold voltagevalue) to a second condition (e.g., a second voltage above the triggerthreshold voltage value), the switch may stop propagating the triggersignal and instead propagate another input signal to the output. Inembodiments, the trigger signal and the other data signal may berelated, such as the trigger signal may indicate a presence of an objectbeing placed on a conveyer and the data signal represents a strainplaced on the conveyer.

In embodiments, to facilitate timely detection of the trigger condition,a rate of sampling of the output of the analog switch may be adjustable,so that, for example, the rate of sampling is higher while the triggersignal is propagated and lower when the data signal is propagated.Alternatively, a rate of sampling may be fixed for either the trigger orthe data signal. In embodiments, the rate of sampling may be based on apredefined time from trigger occurrence to trigger detection and may befaster than a minimum sample rate to capture the data signal.

In embodiments, routing a plurality of hierarchically organized triggersonto another analog channel may facilitate implementing a hierarchicaldata collection triggering structure in an industrial environment. Adata collection template to implement a hierarchical trigger signalarchitecture may include signal switch configuration and function datathat may facilitate a signal switch facility, such as an analogcrosspoint switch or multiplexer to output a first input trigger in ahierarchy, and based on the first trigger condition being detected,output a second input trigger in the hierarchy on the same output as thefirst input trigger by changing an internal mapping of inputs tooutputs. Upon detection of the second input trigger condition, theoutput may be switched to a data signal, such as data from a sensor inan industrial environment.

In embodiments, upon detection of a trigger condition, in addition toswitching from the trigger signal to a data signal, an alarm may begenerated and optionally propagated to a higher functioningdevice/module. In addition to switching to a data signal, upon detectionof a state of the trigger, sensors that otherwise may be disabled orpowered down may be energized/activated to begin to produce data for thenewly selected data signal. Activating might alternatively includesending a reset or refresh signal to the sensor(s).

In embodiments, a system for data collection in an industrialenvironment may include a system for routing a trigger signal onto adata signal path in association with a gearbox of an industrial vehicle.Combining a trigger signal onto a signal path that is also used for adata signal may be useful in gearbox applications by reducing the numberof signal lines that need to be routed, while enabling advancedfunctions, such as data collection based on pressure changes in thehydraulic fluid and the like. As an example, a sensor may be configuredto detect a pressure difference in the hydraulic fluid that exceeds acertain threshold as may occur when the hydraulic fluid flow is directedback into the impeller to give higher torque at low speeds. The outputof such a sensor may be configured as a trigger for collecting dataabout the gearbox when operating at low speeds. In an example, a datacollection system for an industrial environment may have a multiplexeror switch that facilitates routing either a trigger or a data channelover a single signal path. Detecting the trigger signal from thepressure sensor may result in a different signal being routed throughthe same line that the trigger signal was routed by switching a set ofcontrols. A multiplexer may, for example, output the trigger signaluntil the trigger signal is detected as indicating that the outputshould be changed to the data signal. As a result of detecting thehigh-pressure condition, a data collection activity may be activated sothat data can be collected using the same line that was recently used bythe trigger signal.

In embodiments, a system for data collection in an industrialenvironment may include a system for routing a trigger signal onto adata signal path in association with a vehicle suspension for truck andcar operation. Vehicle suspension, particularly active suspension mayinclude sensors for detecting road events, suspension conditions, andvehicle data, such as speed, steering, and the like. These conditionsmay not always need to be detected, except, for example, upon detectionof a trigger condition. Therefore, combining the trigger conditionsignal and at least one data signal on a single physical signal routingpath could be implemented. Doing so may reduce costs due to fewerphysical connections required in such a data collection system. In anexample, a sensor may be configured to detect a condition, such as a pothole, to which the suspension must react. Data from the suspension maybe routed along the same signal routing path as this road conditiontrigger signal so that upon detection of the pot hole, data may becollected that may facilitate determining aspects of the suspension'sreaction to the pot hole.

In embodiments, a system for data collection in an industrialenvironment may include a system for routing a trigger signal onto adata signal path in association with a turbine for power generation in apower station. A turbine used for power generation may be retrofittedwith a data collection system that optimizes existing data signal linesto implement greater data collection functions. One such approachinvolves routing new sources of data over existing lines. Whilemultiplexing signals generally satisfies this need, combining a triggersignal with a data signal via a multiplexer or the like can furtherimprove data collection. In an example, a first sensor may include athermal threshold sensor that may measure the temperature of an aspectof a power generation turbine. Upon detection of that trigger (e.g., bythe temperature rising above the thermal threshold), a data collectionsystem controller may send a different data collection signal over thesame line that was used to detect the trigger condition. This may beaccomplished by a controller or the like sensing the trigger signalchange condition and then signaling to the multiplexer to switch fromthe trigger signal to a data signal to be output on the same line as thetrigger signal for data collection. In this example, when a turbine isdetected as having a portion that exceeds its safe thermal threshold, asecondary safety signal may be routed over the trigger signal path andmonitored for additional safety conditions, such as overheating and thelike.

Referring to FIG. 46, an embodiment of routing a trigger signal over adata signal path in a data collection system in an industrialenvironment is depicted. Signal multiplexer 7400 may receive a triggersignal on a first input from a sensor or other trigger source 7404 and adata signal on a second input from a sensor for detecting a temperatureassociated with an industrial machine in the environment 7402. Themultiplexer 7400 may be configured to output the trigger signal onto anoutput signal path 7406. A data collection module 7410 may process thesignal on the data path 7406 looking for a change in the signalindicative of a trigger condition provided from the trigger sensor 7404through the multiplexer 7400. Upon detection, a control output 7408 maybe changed and thereby control the multiplexer 7400 to start outputtingdata from the temperature probe 7402 by switching an internal switch orthe like that may control one or more of the inputs that may be routedto the output 7406. Data collection facility 7410 may activate a datacollection template in response to the detected trigger that may includeswitching the multiplexer and collecting data into triggered datastorage 7412. Upon completion of the data collection activity,multiplexer control signal 7408 may revert to its initial condition sothat trigger sensor 7404 may be monitored again.

An example system for data collection in an industrial environmentincludes an analog switch that directs a first input to an output of theanalog switch until such time as the output of the analog switchindicates that a second input should be directed to the output of theanalog switch. In certain further embodiments, the example systemincludes: where the output of the analog switch indicated that thesecond input should be directed to the output based on the outputtransitioning from a pending condition to a triggered condition; whereinthe triggered condition includes detecting the output presenting avoltage above a trigger voltage value; routing a number of signals withthe analog switch from inputs on the analog switch to outputs on theanalog switch in response to the output of the analog switch indicatingthat the second input should be directed to the output; sampling theoutput of the analog switch at a rate that exceeds a rate of transitionfor a number of signals input to the analog switch; and/or generating analarm signal when the output of the analog switch indicates that asecond input should be directed to the output of the analog switch.

An example system for data collection in an industrial environmentincludes an analog switch that switches between a first input and asecond input based on a condition of the first input. In certain furtherembodiments, the condition of the first input comprises the first inputpresenting a triggered condition, and/or the triggered conditionincludes detecting the first input presenting a voltage above a triggervoltage value. In certain embodiments, the analog switch includesrouting a plurality of signals with the analog from inputs on the analogswitch to outputs on the analog switch based on the condition of thefirst input, sampling an input of the analog switch at a rate thatexceeds a rate of transition for a plurality of signals input to theanalog switch, and/or generating an alarm signal based on the conditionof the first input.

An example system for data collection in an industrial environmentincludes a trigger signal and at least one data signal that share acommon output of a signal multiplexer, and upon detection of apredefined state of the trigger signal, the common output is configuredto propagate the at least one data signal through the signalmultiplexer. In certain further embodiments, the signal multiplexer isan analog multiplexer, the predefined state of the trigger signal isdetected on the common output, detection of the predefined state of thetrigger signal includes detecting the common output presenting a voltageabove a trigger voltage value, the multiplexer includes routing aplurality of signals with the multiplexer from inputs on the multiplexerto outputs on the multiplexer in response to detection of the predefinedstate of the trigger signal, the multiplexer includes sampling theoutput of the multiplexer at a rate that exceeds a rate of transitionfor a plurality of signals input to the multiplexer, the multiplexerincludes generating an alarm in response to detection of the predefinedstate of the trigger signal, and/or the multiplexer includes activatingat least one sensor to produce the at least one data signal. Withoutlimitation, example systems include: monitoring a gearbox of anindustrial vehicle by directing a trigger signal representing acondition of the gearbox to an output of the analog switch until suchtime as the output of the analog switch indicates that a second inputrepresenting a condition of the gearbox related to the trigger signalshould be directed to the output of the analog switch; monitoring asuspension system of an industrial vehicle by directing a trigger signalrepresenting a condition of the suspension to an output of the analogswitch until such time as the output of the analog switch indicates thata second input representing a condition of the suspension related to thetrigger signal should be directed to the output of the analog switch;and/or monitoring a power generation turbine by directing a triggersignal representing a condition of the power generation turbine to anoutput of the analog switch until such time as the output of the analogswitch indicates that a second input representing a condition of thepower generation turbine related to the trigger signal should bedirected to the output of the analog switch.

In embodiments, a system for data collection in an industrialenvironment may include a data collection system that monitors at leastone signal for a set of collection band parameters and upon detection ofa parameter from the set of collection band parameters in the signal,configures collection of data from a set of sensors based on thedetected parameter. The set of selected sensors, the signal, and the setof collection band parameters may be part of a smart bands datacollection template that may be used by the system when collecting datain an industrial environment. A motivation for preparing a smart-bandsdata collection template may include monitoring a set of conditions ofan industrial machine to facilitate improved operation, reduce downtime, preventive maintenance, failure prevention, and the like. Based onanalysis of data about the industrial machine, such as those conditionsthat may be detected by the set of sensors, an action may be taken, suchas notifying a user of a change in the condition, adjusting operatingparameters, scheduling preventive maintenance, triggering datacollection from additional sets of sensors, and the like. An example ofdata that may indicate a need for some action may include changes thatmay be detectable through trends present in the data from the set ofsensors. Another example is trends of analysis values derived from theset of sensors.

In embodiments, the set of collection band parameters may include valuesreceived from a sensor that is configured to sense a condition of theindustrial machine (e.g., bearing vibration). However, a set ofcollection band parameters may instead be a trend of data received fromthe sensor (e.g., a trend of bearing vibration across a plurality ofvibration measurements by a bearing vibration sensor). In embodiments, aset of collection band parameters may be a composite of data and/ortrends of data from a plurality of sensors (e.g., a trend of data fromon-axis and off-axis vibration sensors). In embodiments, when a datavalue derived from one or more sensors as described herein issufficiently close to a value of data in the set of collection bandparameters, the data collection activity from the set of sensors may betriggered. Alternatively, a data collection activity from the set ofsensors may be triggered when a data value derived from the one or moresensors (e.g., trends and the like) falls outside of a set of collectionband parameters. In an example, a set of data collection band parametersfor a motor may be a range of rotational speeds from 95% to 105% of aselect operational rotational speed. So long as a trend of rotationalspeed of the motor stays within this range, a data collection activitymay be deferred. However, when the trend reaches or exceeds this range,then a data collection activity, such as one defined by a smart bandsdata collection template may be triggered.

In embodiments, triggering a data collection activity, such as onedefined by a smart bands data collection template, may result in achange to a data collection system for an industrial environment thatmay impact aspects of the system such as data sensing, switching,routing, storage allocation, storage configuration, and the like. Thischange to the data collection system may occur in near real time to thedetection of the condition; however, it may be scheduled to occur in thefuture. It may also be coordinated with other data collection activitiesso that active data collection activities, such as a data collectionactivity for a different smart bands data collection template, cancomplete prior to the system being reconfigured to meet the smart bandsdata collection template that is triggered by the sensed conditionmeeting the smart bands data collection trigger.

In embodiments, processing of data from sensors may be cumulative overtime, over a set of sensors, across machines in an industrialenvironment, and the like. While a sensed value of a condition may besufficient to trigger a smart bands data collection template activity,data may need to be collected and processed over time from a pluralityof sensors to generate a data value that may be compared to a set ofdata collection band parameters for conditionally triggering the datacollection activity. Using data from multiple sensors and/or processingdata, such as to generate a trend of data values and the like mayfacilitate preventing inconsequential instances of a sensed data valuebeing outside of an acceptable range from causing unwarranted smartbands data collection activity. In an example, if a vibration from abearing is detected outside of an acceptable range infrequently, thentrending for this value over time may be useful to detect if thefrequency is increasing, decreasing, or staying substantially constantor within a range of values. If the frequency of such a value is foundto be increasing, then such a trend is indicative of changes occurringin operation of the industrial machine as experienced by the bearing. Anacceptable range of values of this trended vibration value may beestablished as a set of data collection band parameters against whichvibration data for the bearing will be monitored. When the trendedvibration value is outside of this range of acceptable values, a smartbands data collection activity may be activated.

In embodiments, a system for data collection in an industrialenvironment that supports smart band data collection templates may beconfigured with data processing capability at a point of sensing of oneor more conditions that may trigger a smart bands data collectiontemplate data collection activity, such as: by use of an intelligentsensor that may include data processing capabilities; by use of aprogrammable logic component that interfaces with a sensor and processesdata from the sensor; by use of a computer processor, such as amicroprocessor and the like disposed proximal to the sensor; and thelike. In embodiments, processing of data collected from one or moresensors for detecting a smart bands template data collection activitymay be performed by remote processors, servers, and the like that mayhave access to data from a plurality of sensors, sensor modules,industrial machines, industrial environments, and the like.

In embodiments, a system for data collection in an industrialenvironment may include a data collection system that monitors anindustrial environment for a set of parameters, and upon detection of atleast one parameter, configures the collection of data from a set ofsensors and causes a data storage controller to adapt a configuration ofdata storage facilities to support collection of data from the set ofsensors based on the detected parameter. The methods and systemsdescribed herein for conditionally changing a configuration of a datacollection system in an industrial environment to implement a smartbands data collection template may further include changes to datastorage architectures. As an example, a data storage facility may bedisposed on a data collection module that may include one or moresensors for monitoring conditions in an industrial environment. Thislocal data storage facility may typically be configured for rapidmovement of sensed data from the module to a next level sensing orprocessing module or server. When a smart bands data collectioncondition is detected, sensor data from a plurality of sensors may needto be captured concurrently. To accommodate this concurrent collection,the local memory may be reconfigured to capture data from each of theplurality of sensors in a coordinated manner, such as repeatedlysampling each of the sensors synchronously, or with a known offset, andthe like, to build up a set of sensed data that may be much larger thanwould typically be captured and moved through the local memory. Astorage control facility for controlling the local storage may monitorthe movement of sensor data into and out of the local data storage,thereby ensuring safe movement of data from the plurality of sensors tothe local data storage and on to a destination, such as a server,networked storage facility, and the like. The local data storagefacility may be configured so that data from the set of sensorsassociated with a smart bands data collection template are securelystored and readily accessible as a set of smart band data to facilitateprocessing the smart band-specific data. As an example, local storagemay comprise non-volatile memory (NVM). To prepare for data collectionin response to a smart band data collection template being triggered,portions of the NVM may be erased to prepare the NVM to receive data asindicated in the template.

In embodiments, multiple sensors may be arranged into a set of sensorsfor condition-specific monitoring. Each set, which may be a logical setof sensors, may be selected to provide information about elements in anindustrial environment that may provide insight into potential problems,root causes of problems, and the like. Each set may be associated with acondition that may be monitored for compliance with an acceptable rangeof values. The set of sensors may be based on a machine architecture,hierarchy of components, or a hierarchy of data that contributes to afinding about a machine that may usefully be applied to maintaining orimproving performance in the industrial environment. Smart band sensorsets may be configured based on expert system analysis of complexconditions, such as machine failures and the like. Smart band sensorsets may be arranged to facilitate knowledge gathering independent of aparticular failure mode or history. Smart band sensor sets may bearranged to test a suggested smart band data collection template priorto implementing it as part of an industrial machine operations program.Gathering and processing data from sets of sensors may facilitatedetermining which sensors contribute meaningful data to the set, andthose sensors that do not contribute can be removed from the set. Smartband sensor sets may be adjusted based on external data, such asindustry studies that indicate the types of sensor data that is mosthelpful to reduce failures in an industrial environment.

In embodiments, a system for data collection in an industrialenvironment may include a data collection system that monitors at leastone signal for compliance to a set of collection band conditions andupon detection of a lack of compliance, configures the collection ofdata from a predetermined set of sensors associated with the monitoredsignal. Upon detection of a lack of compliance, a collection bandtemplate associated with the monitored signal may be accessed, andresources identified in the template may be configured to perform thedata collection. In embodiments, the template may identify sensors toactivate, data from the sensors to collect, duration of collection orquantity of data to be collected, destination (e.g., memory structure)to store the collected data, and the like. In embodiments, a smart bandmethod for data collection in an industrial environment may includeperiodic collection of data from one or more sensors configured to sensea condition of an industrial machine in the environment. The collecteddata may be checked against a set of criteria that define an acceptablerange of the condition. Upon validation that the collected data iseither approaching one end of the acceptable limit or is beyond theacceptable range of the condition, data collection may commence from asmart-band group of sensors associated with the sensed condition basedon a smart-band collection protocol configured as a data collectiontemplate. In embodiments, an acceptable range of the condition is basedon a history of applied analytics of the condition. In embodiments, uponvalidation of the acceptable range being exceeded, data storageresources of a module in which the sensed condition is detected may beconfigured to facilitate capturing data from the smart band group ofsensors.

In embodiments, monitoring a condition to trigger a smart band datacollection template data collection action may be: in response to: aregulation, such as a safety regulation; in response to an upcomingactivity, such as a portion of the industrial environment being shutdown for preventive maintenance; in response to sensor data missing fromroutine data collection activities; and the like. In embodiments, inresponse to a faulty sensor or sensor data missing from a smart bandtemplate data collection activity, one or more alternate sensors may betemporarily included in the set of sensors so as to provide data thatmay effectively substitute for the missing data in data processingalgorithms.

In embodiments, smart band data collection templates may be configuredfor detecting and gathering data for smart band analysis coveringvibration spectra, such as vibration envelope and current signature forspectral regions or peaks that may be combinations of absolute frequencyor factors of machine related parameters, vibration time waveforms fortime-domain derived calculations including, without limitation: RMSoverall, peak overall, true peak, crest factor, and the like; vibrationvectors, spectral energy humps in various regions (e.g., low-frequencyregion, high frequency region, low orders, and the like);pressure-volume analysis and the like.

In embodiments, a system for data collection that applies smart banddata collection templates may be applied to an industrial environment,such as ball screw actuators in an automated production environment.Smart band analysis may be applied to ball screw actuators in industrialenvironments such as precision manufacturing or positioning applications(e.g., semiconductor photolithography machines, and the like). As atypical primary objective of using a ball screw is for precisepositioning, detection of variation in the positioning mechanism canhelp avoid costly defective production runs. Smart bands triggering anddata collection may help in such applications by detecting, throughsmart band analysis, potential variations in the positioning mechanismsuch as in the ball screw mechanism, a worm drive, a linear motor, andthe like. In an example, data related to a ball screw positioning systemmay be collected with a system for data collection in an industrialenvironment as described herein. A plurality of sensors may beconfigured to collect data such as screw torque, screw direction, screwspeed, screw step, screw home detection, and the like. Some portion ofthis data may be processed by a smart bands data analysis facility todetermine if variances, such as trends in screw speed as a function oftorque, approach or exceed an acceptable threshold. Upon such adetermination, a data collection template for the ball screw productionsystem may be activated to configure the data sensing, routing, andcollection resources of the data collection system to perform datacollection to facilitate further analysis. The smart band datacollection template facilitates rapid collection of data from othersensors than screw speed and torque, such as position, direction,acceleration, and the like by routing data from corresponding sensorsover one or more signal paths to a data collector. The duration andorder of collection of the data from these sources may be specified inthe smart bands data collection template so that data required forfurther analysis is effectively captured.

In embodiments, a system for data collection that applies smart banddata collection templates to configure and utilize data collection androuting infrastructure may be applied to ventilation systems in miningenvironments. Ventilation provides a crucial role in mining safety.Early detection of potential problems with ventilation equipment can beaided by applying a smart bands approach to data collection in such anenvironment. Sensors may be disposed for collecting information aboutventilation operation, quality, and performance throughout a miningoperation. At each ventilation device, ventilation-related elements,such as fans, motors, belts, filters, temperature gauges, voltage,current, air quality, poison detection, and the like may be configuredwith a corresponding sensor. While variation in any one element (e.g.,air volume per minute, and the like) may not be indicative of a problem,smart band analysis may be applied to detect trends over time that maybe suggestive of potential problems with ventilation equipment. Toperform smart bands analysis, data from a plurality of sensors may berequired to form a basis for analysis. By implementing data collectionsystems for ventilation stations, data from a ventilation system may becaptured. In an example, a smart band analysis may be indicated for aventilation station. In response to this indication, a data collectionsystem may be configured to collect data by routing data from sensorsdisposed at the ventilation station to a central monitoring facilitythat may gather and analyze data from several ventilation stations.

In embodiments, a system for data collection that applies smart banddata collection templates to configure and utilize data collection androuting infrastructure may be applied to drivetrain data collection andanalysis in mining environments. A drivetrain, such as a drivetrain fora mining vehicle, may include a range of elements that could benefitfrom use of the methods and systems of data collection in an industrialenvironment as described herein. In particular, smart band-based datacollection may be used to collect data from heavy duty mining vehicledrivetrain under certain conditions that may be detectable by smartbands analysis. A smart bands-based data collection template may be usedby a drivetrain data collection and routing system to configure sensors,data paths, and data collection resources to perform data collectionunder certain circumstances, such as those that may indicate anunacceptable trend of drivetrain performance. A data collection systemfor an industrial drivetrain may include sensing aspects of anon-steering axle, a planetary steering axle, driveshafts, (e.g., mainand wing shafts), transmissions, (e.g., standard, torque converters,long drop), and the like. A range of data related to these operationalparts may be collected. However, data for support and structural membersthat support the drivetrain may also need to be collected for thoroughsmart band analysis. Therefore, collection across this wide range ofdrivetrain-related components may be triggered based on a smart bandanalysis determination of a need for this data. In an example, a smartband analysis may indicate potential slippage between a main and wingdriveshaft that may represented by an increasing trend in response delaytime of the wing drive shaft to main drive shaft operation. In responseto this increasing trend, data collection modules disposed throughoutthe mining vehicle's drive train may be configured to route data fromlocal sensors to be collected and analyzed by data collectors. Miningvehicle drivetrain smart based data collection may include a range oftemplates based on which type of trend is detected. If a trend relatedto a steering axle is detected, a data collection template to beimplemented may be different in sensor content, duration, and the likethan for a trend related to power demand for a normalized payload. Eachtemplate could configure data sensing, routing, and collection resourcesthroughout the vehicle drive train accordingly.

Referring to FIG. 47, a system for data collection in an industrialenvironment that facilitates data collection for smart band analysis isdepicted. A system for data collection in an industrial environment mayinclude a smart band analysis data collection template repository 7600in which smart band templates 7610 for data collection systemconfiguration and collection of data may be stored and accessed by adata collection controller 7602. The templates 7610 may include datacollection system configuration 7604 and operation information 7606 thatmay identify sensors, collectors, signal paths, and information forinitiation and coordination of collection, and the like. A controller7602 may receive an indication, such as a command from a smart bandanalysis facility 7608 to select and implement a specific smart bandtemplate 7610. The controller 7602 may access the template 7610 andconfigure the data collection system resources based on the informationin that template. In embodiments, the template may identify: specificsensors; a multiplexer/switch configuration, data collectiontrigger/initiation signals and/or conditions, time duration and/oramount of data for collection; destination of collected data;intermediate processing, if any; and any other useful information,(e.g., instance identifier, and the like). The controller 7602 mayconfigure and operate the data collection system to perform thecollection for the smart band template and optionally return the systemconfiguration to a previous configuration.

An example system for data collection in an industrial environmentincludes a data collection system that monitors at least one signal fora set of collection band parameters and, upon detection of a parameterfrom the set of collection band parameters, configures portions of thesystem and performs collection of data from a set of sensors based onthe detected parameter. In certain further embodiments, the signalincludes an output of a sensor that senses a condition in the industrialenvironment, where the set of collection band parameters comprisesvalues derivable from the signal that are beyond an acceptable range ofvalues derivable from the signal; where the at least one signal includesan output of a sensor that senses a condition in the industrialenvironment; wherein configuring portions of the system includesconfiguring a storage facility to accept data collected from the set ofsensors; where configuring portions of the system includes configuring adata routing portion includes at least one of: an analog crosspointswitch, a hierarchical multiplexer, an analog-to-digital converter, anintelligent sensor, and/or a programmable logic component; whereindetection of a parameter from the set of collection band parameterscomprises detecting a trend value for the signal being beyond anacceptable range of trend values; and/or where configuring portions ofthe system includes implementing a smart band data collection templateassociated with the detected parameter. In certain embodiments, a datacollection system monitors a signal for data values within a set ofacceptable data values that represent acceptable collection bandconditions for the signal and, upon detection of a data value for the atleast one signal outside of the set of acceptable data values, triggersa data collection activity that causes collecting data from apredetermined set of sensors associated with the monitored signal. Incertain further embodiment, a data collection system includes the signalincluding an output of a sensor that senses a condition in theindustrial environment; where the set of acceptable data value includesvalues derivable from the signal that are within an acceptable range ofvalues derivable from the signal; configuring a storage facility of thesystem to facilitate collecting data from the predetermined set ofsensors in response to the detection of a data value outside of the setof acceptable data values; configuring a data routing portion of thesystem including an analog crosspoint switch, a hierarchicalmultiplexer, an analog-to-digital converter, an intelligent sensor,and/or a programmable logic component in response to detecting a datavalue outside of the set of acceptable data values; where detection of adata value for the signal outside of the set of acceptable data valuesincludes detecting a trend value for the signal being beyond anacceptable range of trend values; and/or where the data collectionactivity is defined by a smart band data collection template associatedwith the detected parameter.

An example method for data collection in an industrial environmentcomprising includes an operation to collect data from sensor(s)configured to sense a condition of an industrial machine in theenvironment; an operation to check the collected data against a set ofcriteria that define an acceptable range of the condition; and inresponse to the collected data violating the acceptable range of thecondition, an operation to collect data from a smart-band group ofsensors associated with the sensed condition based on a smart-bandcollection protocol configured as a smart band data collection template.In certain further embodiments, a method includes where violating theacceptable range of the condition includes a trend of the data from thesensor(s) approaching a maximum value of the acceptable range; where thesmart-band group of sensors is defined by the smart band data collectiontemplate; where the smart band data collection template includes a listof sensors to activate, data from the sensors to collect, duration ofcollection of data from the sensors, and/or a destination location forstoring the collected data; where collecting data from a smart-bandgroup of sensors includes configuring at least one data routing resourceof the industrial environment that facilitates routing data from thesmart band group of sensors to a plurality of data collectors; and/orwhere the set of criteria includes a range of trend values derived byprocessing the data from sensor(s).

Without limitation, an example system monitors a ball screw actuator inan automated production environment, and monitors at least one signalfrom the ball screw actuator for a set of collection band parametersand, upon detection of a parameter from the set of collection bandparameters, configures portions of the system and performs collection ofdata from a set of sensors disposed to monitor conditions of the ballscrew actuator based on the detected parameter; another example systemmonitors a ventilation system in a mining environment, and monitors atleast one signal from the ventilation system for a set of collectionband parameters and, upon detection of a parameter from the set ofcollection band parameters, configures portions of the system andperforms collection of data from a set of sensors disposed to monitorconditions of the ventilation system based on the detected parameter; anexample system monitors a drivetrain of a mining vehicle, and monitorsat least one signal from the drive train for a set of collection bandparameters and, upon detection of a parameter from the set of collectionband parameters, configures portions of the system and performscollection of data from a set of sensors disposed to monitor conditionsof the drivetrain based on the detected parameter.

In embodiments, a system for data collection in an industrialenvironment may automatically configure local and remote data collectionresources and may perform data collection from a plurality of systemsensors that are identified as part of a group of sensors that producedata that is required to perform operational deflection shape rendering.In embodiments, the system sensors are distributed throughout structuralportions of an industrial machine in the industrial environment. Inembodiments, the system sensors sense a range of system conditionsincluding vibration, rotation, balance, friction, and the like. Inembodiments, automatically configuring is in response to a condition inthe environment being detected outside of an acceptable range ofcondition values. In embodiments, a sensor in the identified group ofsystem sensors senses the condition.

In embodiments, a system for data collection in an industrialenvironment may configure a data collection plan, such as a template, tocollect data from a plurality of system sensors distributed throughout amachine to facilitate automatically producing an operational deflectionshape visualization (“ODSV”) based on machine structural information anda data set used to produce an ODSV of the machine.

In embodiments, a system for data collection in an industrialenvironment may configure a data collection template for collecting datain an industrial environment by identifying sensors disposed for sensingconditions of preselected structural members of an industrial machine inthe environment based on an ODSV of the industrial machine. Inembodiments, the template may include an order and timing of datacollection from the identified sensors.

In embodiments, methods and systems for data collection in an industrialenvironment may include a method of establishing an acceptable range ofsensor values for a plurality of industrial machine condition sensors byvalidating an operational deflection shape visualization of structuralelements of the machine as exhibiting deflection within an acceptablerange, wherein data from the plurality of sensors used in the validatedODSV define the acceptable range of sensor values.

In embodiments, a system for data collection in an industrialenvironment may include a plurality of data sources, such as sensors,that may be grouped for coordinated data collection to provide datarequired to produce an ODSV. Information regarding the sensors to group,data collection coordination requirements, and the like may be retrievedfrom an ODSV data collection template. Coordinated data collection mayinclude concurrent data collection. To facilitate concurrent datacollection from a portion of the group of sensors, sensor routingresources of the system for data collection may be configured, such asby configuring a data multiplexer to route data from the portion of thegroup of sensors to which it connects to data collectors. Inembodiments, each such source that connects an input of the multiplexermay be routed within the multiplexer to separate outputs so that datafrom all of the connected sources may be routed on to data collectionelements of the industrial environment. In embodiments, the multiplexermay include data storage capabilities that may facilitate sharing acommon output for at least a portion of the inputs. In embodiments, amultiplexer may include data storage capabilities and data bus-enabledoutputs so that data for each source may be captured in a memory andtransmitted over a data bus, such as a data bus that is common to theoutputs of the multiplexer. In embodiments, sensors may be smart sensorsthat may include data storage capabilities and may send data from thedata storage to the multiplexer in a coordinated manner that supportsuse of a common output of the multiplexer and/or use of a common databus.

In embodiments, a system for data collection in an industrialenvironment may comprise templates for configuring the data collectionsystem to collect data from a plurality of sensors to perform ODSV for aplurality of deflection shapes. Individual templates may be configuredfor visualization of looseness, soft joints, bending, twisting, and thelike. Individual deflection shape data collection templates may beconfigured for different portions of a machine in an industrialenvironment.

In embodiments, a system for data collection in an industrialenvironment may facilitate operational deflection shape visualizationthat may include visualization of locations of sensors that contributeddata to the visualization. In the visualization, each sensor thatcontributed data to generate the visualization may be indicated by avisual element. The visual element may facilitate user access toinformation about the sensor, such as location, type, representativedata contributed, path of data from the sensor to a data collector, adeflection shape template identifier, a configuration of a switch ormultiplexer through which the data is routed, and the like. The visualelement may be determined by associating sensor identificationinformation received from a sensor with information, such as a sensormap, that correlates sensor identification information with physicallocation in the environment. The information may appear in thevisualization in response to the visual element representing the sensorbeing selected, such as by a user positioning a cursor on the sensorvisual element.

In embodiments, ODSV may benefit from data satisfying a phaserelationship requirement. A data collection system in the environmentmay be configured to facilitate collecting data that complies with thephase relationship requirement. Alternatively, the data collectionsystem may be configured to collect data from a plurality of sensorsthat contains data that satisfies the phase relationship requirementsbut may also include data that does not. A post processing operationthat may access phase detection data may select a subset of thecollected data.

In embodiments, a system for data collection in an industrialenvironment may include a multiplexer receiving data from a plurality ofsensors and multiplexing the received data for delivery to a datacollector. The data collector may process the data to facilitate ODSV.ODSV may require data from several different sensors, and may benefitfrom using a reference signal, such as data from a sensor, whenprocessing data from the different sensors. The multiplexer may beconfigured to provide data from the different sensors, such as byswitching among its inputs over time so that data from each sensor maybe received by the data collector. However, the multiplexer may includea plurality of outputs so that at least a portion of the inputs may berouted to least two of the plurality of outputs. Therefore, inembodiments, a multiple output multiplexer may be configured tofacilitate data collection that may be suitable for ODSV by routing areference signal from one of its inputs (e.g., data from anaccelerometer) to one of its outputs and multiplexing data from aplurality of its outputs onto one or more of its outputs whilemaintaining the reference signal output routing. A data collector maycollect the data from the reference output and use that to align themultiplexed data from the other sensors.

In embodiments, a system for data collection in an industrialenvironment may facilitate ODSV through coordinated data collectionrelated to conveyors for mining applications. Mining operations may relyon conveyor systems to move material, supplies, and equipment into andout of a mine. Mining operations may typically operate around the clock;therefore, conveyor downtime may have a substantive impact onproductivity and costs. Advanced analysis of conveyor and relatedsystems that focuses on secondary affects that may be challenging todetect merely through point observation may be more readily detected viaODSV. Capturing operational data related to vibration, stresses, and thelike can facilitate ODSV. However, coordination of data capture providesmore reliable results. Therefore, a data collection system that may havesensors dispersed throughout a conveyor system can be configured tofacilitate such coordinated data collection. In an example, capture ofdata affecting structural components of a conveyor, such as; landingpoints and the horizontal members that connect them and support theconveyer between landing points; conveyer segment handoff points; motormounts; mounts of conveyer rollers and the like may need to becoordinated with data related to conveyor dynamic loading, drivesystems, motors, gates, and the like. A system for data collection in anindustrial environment, such as a mining environment may include datasensing and collection modules placed throughout the conveyor atlocations such as segment handoff points, drive systems, and the like.Each module may be configured by one or more controllers, such asprogrammable logic controllers, that may be connected through a physicalor logical (e.g., wireless) communication bus that aids in performingcoordinated data collection. To facilitate coordination, a referencesignal, such as a trigger and the like, may be communicated among themodules for use when collecting data. In embodiments, data collectionand storage may be performed at each module so as to reduce the need forreal-time transfer of sensed data throughout the mining environment.Transfer of data from the modules to an ODSV processing facility may beperformed after collection, or as communication bandwidth between themodules and the processing facility allows. ODSV can provide insightinto conditions in the conveyer, such as deflection of structuralmembers that may, over time cause premature failure. Coordinated datacollection with a data collection system for use in an industrialenvironment, such as mining, can enable ODSV that may reduce operatingcosts by reducing downtime due to unexpected component failure.

In embodiments, a system for data collection in an industrialenvironment may facilitate operational deflection shape visualizationthrough coordinated data collection related to fans for miningapplications. Fans provide a crucial function in mining operations ofmoving air throughout a mine to provide ventilation, equipment cooling,combustion exhaust evacuation, and the like. Ensuring reliable and oftencontinuous operation of fans may be critical for miner safety andcost-effective operations. Dozens or hundreds of fans may be used inlarge mining operations. Fans, such as fans for ventilation managementmay include circuit, booster, and auxiliary types. High capacityauxiliary fans may operate at high speeds, over 2500 RPMs. PerformingODSV may reveal important reliability information about fans deployed ina mining environment. Collecting the range of data needed for ODSV ofmining fans may be performed by a system for collecting data inindustrial environments as described herein. In embodiments, sensingelements, such as intelligent sensing and data collection modules may bedeployed with fans and/or fan subsystems. These modules may exchangecollection control information (e.g., over a dedicated control bus andthe like) so that data collection may be coordinated in time and phaseto facilitate ODSV.

A large auxiliary fan for use in mining may be constructed fortransportability into and through the mine and therefore may include afan body, intake and outlet ports, dilution valves, protection cage,electrical enclosure, wheels, access panels, and other structural and/oroperational elements. The ODSV of such an auxiliary fan may requirecollection of data from many different elements. A system for datacollection may be configured to sense and collect data that may becombined with structural engineering data to facilitate ODSV for thistype of industrial fan.

Referring to FIG. 48, an embodiment of a system for data collection inan industrial environment that performs coordinated data collectionsuitable for ODSV is depicted. A system for data collection in anindustrial environment may include a ODSV data collection templaterepository 7800 in which ODSV templates 7810 for data collection systemconfiguration and collection of data may be stored and accessed by asystem for data collection controller 7802. The templates 7810 mayinclude data collection system configuration 7804 and operationinformation 7806 that may identify sensors, collectors, signal paths,reference signal information, information for initiation andcoordination of collection, and the like. A controller 7802 may receivean indication, such as a command from a ODSV analysis facility 7808 toselect and implement a specific ODSV template 7810. The controller 7802may access the template 7810 and configure the data collection systemresources based on the information in that template. In embodiments, thetemplate may identify specific sensors, multiplexer/switchconfiguration, reference signals for coordinating data collection, datacollection trigger/initiation signals and/or conditions, time duration,and/or amount of data for collection, destination of collected data,intermediate processing, if any, and any other useful information (e.g.,instance identifier, and the like). The controller 7802 may configureand operate the data collection system to perform the collection for theODSV template and optionally return the system configuration to aprevious configuration.

An example method of data collection for performing ODSV in anindustrial environment includes automatically configuring local andremote data collection resources and collecting data from a number ofsensors using the configured resources, where the number of sensorsinclude a group of sensors that produce data that is required to performthe ODSV. In certain further embodiments, an example method furtherincludes where the sensors are distributed throughout structuralportions of an industrial machine in the industrial environment; wherethe sensors sense a range of system conditions including vibration,rotation, balance, and/or friction; where the automatically configuringis in response to a condition in the environment being detected outsideof an acceptable range of condition values; where the condition issensed by a sensor in a group of system sensors; where automaticallyconfiguring includes configuring a signal switching resource toconcurrently connect a portion of the group of sensors to datacollection resources; and/or where the signal switching resource isconfigured to maintain a connection between a reference sensor and thedata collection resources throughout a period of collecting data fromthe sensors to perform ODSV.

An example method of data collection in an industrial environmentincludes configuring a data collection plan to collect data from anumber of system sensors distributed throughout a machine in theindustrial environment, the plan based on machine structural informationand an indication of data needed to produce an ODSV of the machine;configuring data sensing, routing and collection resources in theenvironment based on the data collection plan; and collecting data basedon the data collection plan. In certain further embodiments, an examplemethod further includes: producing the ODSV; where the configuring datasensing, routing, and collection resources is in response to a conditionin the environment being detected outside of an acceptable range ofcondition values; where the condition is sensed by a sensor identifiedin the data collection plan; where configuring resources includesconfiguring a signal switching resource to concurrently connect theplurality of system sensors to data collection resources; and/or wherethe signal switching resource is configured to maintain a connectionbetween a reference sensor and the data collection resources throughouta period of collecting data from the sensors to perform ODSV.

An example system for data collection in an industrial environmentincludes: a number of sensors disposed throughout the environment;multiplexer that connects signals from the plurality of sensors to datacollection resources; and a processor for processing data collected fromthe number of sensors in response to the data collection template, wherethe processing results in an ODSV of a portion of a machine disposed inthe environment. In certain further embodiments, an example systemincludes: where the ODSV collection template further identifies acondition in the environment on which performing data collection fromthe identified sensors is dependent; where the condition is sensed by asensor identified in the ODSV data collection template; where the datacollection template specified inputs of the multiplexer to concurrentlyconnect to data collection resources; where the multiplexer isconfigured to maintain a connection between a reference sensor and thedata collection resources throughout a period of collecting data fromthe sensors to perform ODSV; where the ODSV data collection templatespecifies data collection requirements for performing ODSV forlooseness, soft joints, bending, and/or twisting of a portion of amachine in the industrial environment; and/or where the ODSV collectiontemplate specifies an order and timing of data collection from aplurality of identified sensors.

An example method of monitoring a mining conveyer for performing ODSV ofthe conveyer includes automatically configuring local and remote datacollection resources and collecting data from a number of sensorsdisposed to sense the mining conveyor using the configured resources,wherein the plurality of sensors comprise a group of sensors thatproduce data that is required to perform the operational deflectionshape visualization of a portion of the conveyor. An example method ofmonitoring a mining fan for performing ODSV of the fan includesautomatically configuring local and remote data collection resourcescollecting data from a number of sensors disposed to sense the fan usingthe configured resources, and where the number of sensors include agroup of sensors that produce data that is sufficient or required toperform ODSV of a portion of the fan.

In embodiments, a system for data collection in an industrialenvironment may include a hierarchical multiplexer that facilitatessuccessive multiplexing of input data channels according to aconfigurable hierarchy, such as a user configurable hierarchy. Thesystem for data collection in an industrial environment may include thehierarchical multiplexer that facilitates successive multiplexing of aplurality of input data channels according to a configurable hierarchy.The hierarchy may be automatically configured by a controller based onan operational parameter in the industrial environment, such as aparameter of a machine in the industrial environment.

In embodiments, a system for data collection in an industrialenvironment may include a plurality of sensors that may output data atdifferent rates. The system may also include a multiplexer module thatreceives sensor outputs from a first portion of the plurality of sensorswith similar output rates into separate inputs of a first hierarchicalmultiplexer of the multiplexer module. The first hierarchicalmultiplexer of the multiplexer module may provide at least onemultiplexed output of a portion of its inputs to a second hierarchicalmultiplexer that receives sensor outputs from a second portion of theplurality of sensors with similar output rates and that provides atleast one multiplexed output of a portion of its inputs. In embodiments,the output rates of the first set of sensors may be slower than theoutput rates of the second set of sensors. In embodiments, datacollection rate requirements of the first set of sensors may be lowerthan the data collection rate requirements of the second set of sensors.In embodiments, the first hierarchical multiplexer output is atime-multiplexed combination of a portion of its inputs. In embodiments,the second hierarchical multiplexer receives sensor signals with outputrates that are similar to a rate of output of the first multiplexer,wherein the first multiplexer produces time-based multiplexing of theportion of its plurality of inputs.

In embodiments, a system for data collection in an industrialenvironment may include a hierarchical multiplexer that is dynamicallyconfigured based on a data acquisition template. The hierarchicalmultiplexer may include a plurality of inputs and a plurality ofoutputs, wherein any input can be directed to any output in response tosensor output collection requirements of the template, and wherein asubset of the inputs can be multiplexed at a first switching rate andoutput to at least one of the plurality of outputs.

In embodiments, a system for data collection in an industrialenvironment may include a plurality of sensors for sensing conditions ofa machine in the environment, a hierarchical multiplexer, a plurality ofanalog-to-digital converters (ADCs), a processor, local storage, and anexternal interface. The system may use the processor to access a dataacquisition template of parameters for data collection from a portion ofthe plurality of sensors, configure the hierarchical multiplexer, theADCs and the local storage to facilitate data collection based on thedefined parameters, and execute the data collection with the configuredelements including storing a set of data collected from a portion of theplurality of sensors into the local storage. In embodiments, the ADCsconvert analog sensor data into a digital form that is compatible withthe hierarchical multiplexer. In embodiments, the processor monitors atleast one signal generated by the sensors for a trigger condition and,upon detection of the trigger condition, responds by at least one ofcommunicating an alert over the external interface and performing dataacquisition according to a template that corresponds to the triggercondition.

In embodiments, a system for data collection in an industrialenvironment may include a hierarchical multiplexer that may beconfigurable based on a data collection template of the environment. Themultiplexer may support receiving a large number of data signals (e.g.,from sensors in the environment) simultaneously. In embodiments, allsensors for a portion of an industrial machine in the environment may beindividually connected to inputs of a first stage of the multiplexer.The first stage of the multiplexer may provide a plurality of outputsthat may feed into a second multiplexer stage. The second stagemultiplexer may provide multiple outputs that feed into a third stage,and so on. Data collection templates for the environment may beconfigured for certain data collection sets, such as a set to determinetemperature throughout a machine or a set to determine vibrationthroughout a machine, and the like. Each template may identify aplurality of sensors in the environment from which data is to becollected, such as during a data collection event. When a template ispresented to the hierarchical multiplexer, mapping of inputs to outputsfor each multiplexing stage may be configured so that the required datais available at output(s) of a final multiplexing hierarchical stage fordata collection. In an example, a data collection template to collect aset of data to determine temperature throughout a machine in theenvironment may identify many temperature sensors. The first stagemultiplexer may respond to the template by selecting all of theavailable inputs that connect to temperature sensors. The data fromthese sensors maybe multiplexed onto multiple inputs of a second stagesensor that may perform time-based multiplexing to produce atime-multiplexed output(s) of temperature data from a portion of thesensors. These outputs may be gathered by a data collector andde-multiplexed into individual sensor temperature readings.

In embodiments, time-sensitive signals, such as triggers and the like,may connect to inputs that directly connect to a final multiplexerstage, thereby reducing any potential delay caused by routing throughmultiple multiplexing stages.

In embodiments, a hierarchical multiplexer in a system for datacollection in an industrial environment may comprise an array of relays,a programmable logic component, such as a CPLD, a field programmablegate array (FPGA), and the like.

In embodiments, a system for data collection in an industrialenvironment that may include a hierarchical multiplexer for routingsensor outputs onto signal paths may be used with explosive systems inmining applications. Blast initiating and electronic blasting systemsmay be configured to provide computer assisted blasting systems.Ensuring that blasting occurs safely may involve effective sensing andanalysis of a range of conditions. A system for data collection in anindustrial environment may be deployed to sense and collect dataassociated with explosive systems, such as explosive systems used formining. A data collection system can use a hierarchical multiplexer tocapture data from explosive system installations automatically byaligning, for example, a deployment of the explosive system includingits layout plans, integration, interconnectivity, cascading plan, andthe like with the hierarchical multiplexer. An explosive system may bedeployed with a form of hierarchy that starts with a primary initiatorand follows detonation connections through successive layers ofelectronic blast control to sequenced detonation. Data collected fromeach of these layers of blast systems configuration may be associatedwith stages of a hierarchical multiplexer so that data collected frombulk explosive detonation can be captured in a hierarchy thatcorresponds to its blast control hierarchy.

In embodiments, a system for data collection in an industrialenvironment that may include a hierarchical multiplexer for routingsensor outputs onto signal paths may be used with refinery blowers inoil and gas pipeline applications. Refinery blower applications includefired heater combustion air preheat systems and the like. Forced draftblowers may include a range of moving and moveable parts that maybenefit from condition sensing and monitoring. Sensing may includedetecting conditions of: couplings (e.g., temperature, rotational rate,and the like); motors (vibration, temperature, RPMs, torque, powerusage, and the like); louver mechanics (actuators, louvers, and thelike); and plenums (flow rate, blockage, back pressure, and the like). Asystem for data collection in an industrial environment that uses ahierarchical multiplexer for routing signals from sensors and the liketo data collectors may be configured to collect data from a refineryblower. In an example, a plurality of sensors may be deployed to senseair flow into, throughout, and out of a forced draft blower used in arefinery application, such as to preheat combustion air. Sensors may begrouped based on a frequency of a signal produced by sensors. Sensorsthat detect louver position and control may produce data at a lower ratethan sensors that detect blower RPMs. Therefore, louver position andcontrol sensor signals can be applied to a lower stage in a multiplexerhierarchy than the blower RPM sensors because data from louvers changeless often than data from RPM sensors. A data collection system couldswitch among a plurality of louver sensors and still capture enoughinformation to properly detect louver position. However, properlydetecting blower RPM data may require greater bandwidth of connectionbetween the blower RPM sensor and a data collector. A hierarchicalmultiplexer may enable capturing blower RPM data at a rate that isrequired for proper detection (perhaps by outputting the RPM sensor datafor long durations of time), while switching among several louver sensorinputs and directing them onto (or through) an output that is differentthan the blower RPM output. Alternatively, the louver inputs may betime-multiplexed with the blower RPM data onto a single output that canbe de-multiplexed by a data collector that is configured to determinewhen blower RPM data is being output and when louver position data isbeing output.

In embodiments, a system for data collection in an industrialenvironment that may include a hierarchical multiplexer for routingsensor outputs onto signal paths may be used with pipeline-relatedcompressors (e.g., reciprocating) in oil and gas pipeline applications.A typical use of a reciprocating compressor for pipeline application isproduction of compressed air for pipeline testing. A system for datacollection in an industrial environment may apply a hierarchicalmultiplexer while collecting data from a pipeline testing-basedreciprocating compressor. Data from sensors deployed along a portion ofa pipeline being tested may be input to the lowest stage of thehierarchical multiplexer because these sensors may be periodicallysampled prior to and during testing. However, the rate of sampling maybe low relative to sensors that detect compressor operation, such asparts of the compressor that operate at higher frequencies, such as thereciprocating linkage, motor, and the like. The sensors that providedata at frequencies that enable reproduction of the detected motion maybe input to higher stages in the hierarchical multiplexer. Timemultiplexing among the pipeline sensors may provide for coverage of alarge number of sensors while capturing events such as seal leakage andthe like. However, time multiplexing among reciprocating linkage sensorsmay require output signal bandwidth that may exceed the bandwidthavailable for routing data from the multiplexer to a data collector.Therefore, in embodiments, a plurality of pipeline sensors may betime-multiplexed onto a single multiplexer output and a compressorsensor detecting rapidly moving parts, such as the compressor motor, maybe routed to separate outputs of the multiplexer.

Referring to FIG. 49, a system for data collection in an industrialenvironment that uses a hierarchical multiplexer for routing sensorsignals to data collectors is depicted. Outputs from a plurality ofsensors, such as sensors that monitor conditions that change withrelatively low frequency (e.g., blower louver position sensors) may beinput to a lowest hierarchical stage 8000 of a hierarchical multiplexer8002 and routed to successively higher stages in the multiplexer,ultimately being output from the multiplexer, perhaps as atime-multiplexed signal comprising time-specific samples of each of theplurality of low frequency sensors. Outputs from a second plurality ofsensors, such as sensors that monitor motor operation that may run atmore than 1000 RPMs may be input to a higher hierarchical stage 8004 ofthe hierarchical multiplexer and routed to outputs that support therequired bandwidth.

An example system for data collection in an industrial environmentincludes a controller for controlling data collection resources in theindustrial environment and a hierarchical multiplexer that facilitatessuccessive multiplexing of a number of input data channels according toa configurable hierarchy, wherein the hierarchy is automaticallyconfigured by the controller based on an operational parameter of amachine in the industrial environment. In certain further embodiments,an example system includes: where the operational parameter of themachine is identified in a data collection template; where the hierarchyis automatically configured in response to smart band data collectionactivation further including an analog-to-digital converter disposedbetween a source of the input data channels and the hierarchicalmultiplexer; and/or where the operational parameter of the machinecomprises a trigger condition of at least one of the data channels.Another example system for data collection in an industrial environmentincludes a plurality of sensors and a multiplexer module that receivessensor outputs from a first portion of the sensors with similar outputrates into separate inputs of a first hierarchical multiplexer thatprovides at least one multiplexed output of a portion of its inputs to asecond hierarchical multiplexer, the second hierarchical multiplexerreceiving sensor outputs from a second portion of the sensors andproviding at least one multiplexed output of a portion of its inputs. Incertain further embodiments, an example system includes: where thesecond portion of the sensors output data at rates that are higher thanthe output rates of the first portion of the sensors; where the firstportion and the second portion of the sensors output data at differentrates; where the first hierarchical multiplexer output is atime-multiplexed combination of a portion of its inputs; where thesecond multiplexer receives sensor signals with output rates that aresimilar to a rate of output of the first multiplexer; and/or where thefirst multiplexer produces time-based multiplexing of the portion of itsinputs.

An example system for data collection in an industrial environmentincludes a number of sensors for sensing conditions of a machine in theenvironment a hierarchical multiplexer, a number of analog-to-digitalconverters, a controller, local storage, an external interface, wherethe system includes using the controller to access a data acquisitiontemplate that defines parameters for data collection from a portion ofthe sensors, to configure the hierarchical multiplexer, the ADCs, andthe local storage to facilitate data collection based on the definedparameters, and to execute the data collection with the configuredelements including storing a set of data collected from a portion of thesensors into the local storage. In certain further embodiments, anexample system includes: where the ADCs convert analog sensor data intoa digital form that is compatible with the hierarchical multiplexer;where the processor monitors at least one signal generated by thesensors for a trigger condition and, upon detection of the triggercondition, responds by communicating an alert over the externalinterface and/or performing data acquisition according to a templatethat corresponds to the trigger condition; where the hierarchicalmultiplexer performs successive multiplexing of data received from thesensors according to a configurable hierarchy; where the hierarchy isautomatically configured by the controller based on an operationalparameter of a machine in the industrial environment; where theoperational parameter of the machine is identified in a data collectiontemplate; where the hierarchy is automatically configured in response tosmart band data collection activation; the system further including anADC disposed between a source of the input data channels and thehierarchical multiplexer; where the operational parameter of the machineincludes a trigger condition of at least one of the data channels; wherethe hierarchical multiplexer performs successive multiplexing of datareceived from the plurality of sensors according to a configurablehierarchy; and/or where the hierarchy is automatically configured by acontroller based on a detected parameter of an industrial environment.Without limitation, n example system is configured for monitoring amining explosive system, and includes a controller for controlling datacollection resources associated with the explosive system, and ahierarchical multiplexer that facilitates successive multiplexing of anumber of input data channels according to a configurable hierarchy,where the hierarchy is automatically configured by the controller basedon a configuration of the explosive system. Without limitation, anexample system is configured for monitoring a refinery blower in an oiland gas pipeline applications, and includes a controller for controllingdata collection resources associated with the refinery blower, and ahierarchical multiplexer that facilitates successive multiplexing of anumber of input data channels according to a configurable hierarchy,where the hierarchy is automatically configured by the controller basedon a configuration of the refinery blower. Without limitation, anexample system is configured for monitoring a reciprocating compressorin an oil and gas pipeline applications comprising, and includescontroller for controlling data collection resources associated with thereciprocating compressor, and a hierarchical multiplexer thatfacilitates successive multiplexing of a number of input data channelsaccording to a configurable hierarchy, where the hierarchy isautomatically configured by the controller based on a configuration ofthe reciprocating compressor.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by monitoring the condition of various componentsthroughout a process. Monitoring may include monitoring the amplitude ofa sensor signal measuring attributes such as temperature, humidity,acceleration, displacement, and the like. An embodiment of a datamonitoring device 8100 is shown in FIG. 50 and may include a pluralityof sensors 8106 communicatively coupled to a controller 8102. Thecontroller 8102 may include a data acquisition circuit 8104, a dataanalysis circuit 8108, a MUX control circuit 8114, and a responsecircuit 8110. The data acquisition circuit 8104 may include a MUX 8112where the inputs correspond to a subset of the detection values. The MUXcontrol circuit 8114 may be structured to provide adaptive scheduling ofthe logical control of the MUX and the correspondence of MUX input anddetected values based on a subset of the plurality of detection valuesand/or a command from the response circuit 8110 and/or the output of thedata analysis circuit 8104. The data analysis circuit 8108 may compriseone or more of a peak detection circuit, a phase differential circuit, aPLL circuit, a bandpass filter circuit, a frequency transformationcircuit, a frequency analysis circuit, a torsional analysis circuit, abearing analysis circuit, an overload detection circuit, a sensor faultdetection circuit, a vibrational resonance circuit for theidentification of unfavorable interaction among machines or components,a distortion identification circuit for the identification ofunfavorable distortions such as deflections shapes upon operation,overloading of weight, excessive forces, stress and strain-basedeffects, and the like. The data analysis circuit 8108 may output acomponent health status as a result of the analysis.

The data analysis circuit 8108 may determine a state, condition, orstatus of a component, part, subsystem, or the like of a machine,device, system or item of equipment (collectively referred to herein asa component health status) based on a maximum value of a MUX output fora given input or a rate of change of the value of a MUX output for agiven input. The data analysis circuit 8108 may determine a componenthealth status based on a time integration of the value of a MUX for agiven input. The data analysis circuit 8108 may determine a componenthealth status based on phase differential of MUX output relative to anon-board time or another sensor. The data analysis circuit 8108 maydetermine a component health status based on a relationship of value,phase, phase differential, and rate of change for MUX outputscorresponding to one or more input detection values. The data analysiscircuit 8108 may determine a component health status based on processstage or component specification or component anticipated state.

The multiplexer control circuit 8114 may adapt the scheduling of thelogical control of the multiplexer based on a component health status,an anticipated component health status, the type of component, the typeof equipment being measured, an anticipated state of the equipment, aprocess stage (different parameters/sensor values) may be important atdifferent stages in a process. The multiplexer control circuit 8114 mayadapt the scheduling of the logical control of the multiplexer based ona sequence selected by a user or a remote monitoring application, or onthe basis of a user request for a specific value. The multiplexercontrol circuit 8114 may adapt the scheduling of the logical control ofthe multiplexer based on the basis of a storage profile or plan (such asbased on type and availability of storage elements and parameters asdescribed elsewhere in this disclosure and in the documents incorporatedherein by reference), network conditions or availability (also asdescribed elsewhere in this disclosure and in the documents incorporatedherein by reference), or value or cost of component or equipment.

The plurality of sensors 8106 may be wired to ports on the dataacquisition circuit 8104. The plurality of sensors 8106 may bewirelessly connected to the data acquisition circuit 8104. The dataacquisition circuit 8104 may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors8106 where the sensors 8106 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of sensors 8106 for a data monitoringdevice 8100 designed for a specific component or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, resolution desired atvarious positions in a process or plant, reliability of the sensors, andthe like. The impact of a failure, time response of a failure (e.g.,warning time and/or off-nominal modes occurring before failure),likelihood of failure, and/or sensitivity required, and/or difficulty todetect failure conditions may drive the extent to which a component orpiece of equipment is monitored with more sensors, and/or highercapability sensors being dedicated to systems where unexpected orundetected failure would be costly or have severe consequences.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating, and the like, sensors8106 may comprise one or more of, without limitation, a vibrationsensor, a thermometer, a hygrometer, a voltage sensor and/or a currentsensor (for the component and/or other sensors measuring the component),an accelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition, and/or spectral analysis,and/or object position or movement), an image sensor, a structured lightsensor, a laser-based image sensor, a thermal imager, an acoustic wavesensor, a displacement sensor, a turbidity meter, a viscosity meter, anaxial load sensor, a radial load sensor, a tri-axial sensor, anaccelerometer, a speedometer, a tachometer, a fluid pressure meter, anair flow meter, a horsepower meter, a flow rate meter, a fluid particledetector, an optical (laser) particle counter, an ultrasonic sensor, anacoustical sensor, a heat flux sensor, a galvanic sensor, amagnetometer, a pH sensor, and the like, including, without limitation,any of the sensors described throughout this disclosure and thedocuments incorporated by reference.

The sensors 8106 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors8106 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 8106 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

The sensors 8106 may monitor components such as bearings, sets ofbearings, motors, driveshafts, pistons, pumps, conveyors, vibratingconveyors, compressors, drills, and the like in vehicles, oil and gasequipment in the field, in assembly line components, and the like.

In embodiments, as illustrated in FIG. 50, the sensors 8106 may be partof the data monitoring device 8100, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 51 and 52, oneor more external sensors 8126, which are not explicitly part of amonitoring device 8120 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to, or accessed by the monitoring device 8120. The monitoringdevice 8120 may include a controller 8122. The controller 8122 mayinclude a data acquisition circuit 8104, a data analysis circuit 8108, aMUX control circuit 8114, and a response circuit 8110. The dataacquisition circuit 8104 may comprise a MUX 8112 where the inputscorrespond to a subset of the detection values. The MUX control circuit8114 may be structured to provide the logical control of the MUX and thecorrespondence of MUX input and detected values based on a subset of theplurality of detection values and/or a command from the response circuit8110 and/or the output of the data analysis circuit 8108. The dataanalysis circuit 8108 may comprise one or more of a peak detectioncircuit, a phase differential circuit, a PLL circuit, a bandpass filtercircuit, a frequency transformation circuit, a frequency analysiscircuit, a torsional analysis circuit, a bearing analysis circuit, anoverload detection circuit, vibrational resonance circuit for theidentification of unfavorable interaction among machines or components,a distortion identification circuit for the identification ofunfavorable distortions such as deflections shapes upon operation,stress and strain-based effects, and the like.

The one or more external sensors 8126 may be directly connected to theone or more input ports 8128 on the data acquisition circuit 8104 of thecontroller 8122 or may be accessed by the data acquisition circuit 8104wirelessly, such as by a reader, interrogator, or other wirelessconnection, such as over a short-distance wireless protocol. Inembodiments, as shown in FIG. 52, a data acquisition circuit 8104 mayfurther comprise a wireless communication circuit 8130. The dataacquisition circuit 8104 may use the wireless communication circuit 8130to access detection values corresponding to the one or more externalsensors 8126 wirelessly or via a separate source or some combination ofthese methods.

In embodiments, as illustrated in FIG. 53, the controller 8134 mayfurther comprise a data storage circuit 8136. The data storage circuit8136 may be structured to store one or more of sensor specifications,component specifications, anticipated state information, detectedvalues, multiplexer output, component models, and the like. The datastorage circuit 8136 may provide specifications and anticipated stateinformation to the data analysis circuit 8108.

In embodiments, the response circuit 8110 may initiate a variety ofactions based on the sensor status provided by the data analysis circuit8108. The response circuit 8110 may adjust a sensor scaling value (e.g.,from 100 mV/gram to 10 mV/gram). The response circuit 8110 may select analternate sensor from a plurality available. The response circuit 8110may acquire data from a plurality of sensors of different ranges. Theresponse circuit 8110 may recommend an alternate sensor. The responsecircuit 8110 may issue an alarm or an alert.

In embodiments, the response circuit 8110 may cause the data acquisitioncircuit 8104 to enable or disable the processing of detection valuescorresponding to certain sensors based on the component status. This mayinclude switching to sensors having different response rates,sensitivity, ranges, and the like; accessing new sensors or types ofsensors, accessing data from multiple sensors, and the like. Switchingmay be undertaken based on a model, a set of rules, or the like. Inembodiments, switching may be under control of a machine learningsystem, such that switching is controlled based on one or more metricsof success, combined with input data, over a set of trials, which mayoccur under supervision of a human supervisor or under control of anautomated system. Switching may involve switching from one input port toanother (such as to switch from one sensor to another). Switching mayinvolve altering the multiplexing of data, such as combining differentstreams under different circumstances. Switching may involve activatinga system to obtain additional data, such as moving a mobile system (suchas a robotic or drone system), to a location where different oradditional data is available, such as positioning an image sensor for adifferent view or positioning a sonar sensor for a different directionof collection, or to a location where different sensors can be accessed,such as moving a collector to connect up to a sensor at a location in anenvironment by a wired or wireless connection. This switching may beimplemented by directing changes to the multiplexer (MUX) controlcircuit 8114.

In embodiments, the response circuit 8110 may make recommendations forthe replacement of certain sensors in the future with sensors havingdifferent response rates, sensitivity, ranges, and the like. Theresponse circuit 8110 may recommend design alterations for futureembodiments of the component, the piece of equipment, the operatingconditions, the process, and the like.

In embodiments, the response circuit 8110 may recommend maintenance atan upcoming process stop or initiate a maintenance call where themaintenance may include the replacement of the sensor with the same oran alternate type of sensor having a different response rate,sensitivity, range, and the like. In embodiments, the response circuit8110 may implement or recommend process changes—for example to lower theutilization of a component that is near a maintenance interval,operating off-nominally, or failed for purpose but is still at leastpartially operational, to change the operating speed of a component(such as to put it in a lower-demand mode), to initiate amelioration ofan issue (such as to signal for additional lubrication of a rollerbearing set, or to signal for an alignment process for a system that isout of balance), and the like.

In embodiments, the data analysis circuit 8108 and/or the responsecircuit 8110 may periodically store certain detection values and/or theoutput of the multiplexers and/or the data corresponding to the logiccontrol of the MUX in the data storage circuit 8136 to enable thetracking of component performance over time. In embodiments, based onsensor status, as described elsewhere herein, recently measured sensordata and related operating conditions such as RPMs, component loads,temperatures, pressures, vibrations, or other sensor data of the typesdescribed throughout this disclosure in the data storage circuit 8136enable the backing out of overloaded/failed sensor data. The signalevaluation circuit 8108 may store data at a higher data rate for greatergranularity in future processing, the ability to reprocess at differentsampling rates, and/or to enable diagnosing or post-processing of systeminformation where operational data of interest is flagged, and the like.

In embodiments, as shown in FIGS. 54, 55, 56, and 57, a data monitoringsystem 8138 may include at least one data monitoring device 8140. The atleast one data monitoring device 8140 may include sensors 8106 and acontroller 8142 comprising a data acquisition circuit 8104, a dataanalysis circuit 8108, a data storage circuit 8136, and a communicationcircuit 8146 to allow data and analysis to be transmitted to amonitoring application 8150 on a remote server 8148. The signalevaluation circuit 8108 may include at least an overload detectioncircuit (e.g., reference FIGS. 101 and 102) and/or a sensor faultdetection circuit (e.g., reference FIGS. 101 and 102). The signalevaluation circuit 8108 may periodically share data with thecommunication circuit 8146 for transmittal to the remote server 8148 toenable the tracking of component and equipment performance over time andunder varying conditions by a monitoring application 8150. Based on thesensor status, the signal evaluation circuit 8108 and/or responsecircuit 8110 may share data with the communication circuit 8146 fortransmittal to the remote server 8148 based on the fit of data relativeto one or more criteria. Data may include recent sensor data andadditional data such as RPMs, component loads, temperatures, pressures,vibrations, and the like for transmittal. The signal evaluation circuit8108 may share data at a higher data rate for transmittal to enablegreater granularity in processing on the remote server.

In embodiments, as shown in FIG. 54, the communication circuit 8146 maycommunicate data directly to a remote server 8148. In embodiments, asshown in FIG. 55, the communication circuit 8146 may communicate data toan intermediate computer 8152 which may include a processor 8154 runningan operating system 8156 and a data storage circuit 8158.

In embodiments as illustrated in FIGS. 56 and 57, a data collectionsystem 8160 may have a plurality of monitoring devices 8144 collectingdata on multiple components in a single piece of equipment, collectingdata on the same component across a plurality of pieces of equipment,(both the same and different types of equipment) in the same facility,as well as collecting data from monitoring devices in multiplefacilities. A monitoring application 8150 on a remote server 8148 mayreceive and store one or more of detection values, timing signals, anddata coming from a plurality of the various monitoring devices 8144.

In embodiments, as shown in FIG. 56, the communication circuit 8146 maycommunicate data directly to a remote server 8148. In embodiments, asshown in FIG. 57, the communication circuit 8146 may communicate data toan intermediate computer 8152 which may include a processor 8154 runningan operating system 8156 and a data storage circuit 8158. There may bean individual intermediate computer 8152 associated with each monitoringdevice 8140 or an individual intermediate computer 8152 may beassociated with a plurality of monitoring devices 8144 where theintermediate computer 8152 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server8148. Communication to the remote server 8148 may be streaming, batch(e.g., when a connection is available), or opportunistic.

The monitoring application 8150 may select subsets of the detectionvalues to be jointly analyzed. Subsets for analysis may be selectedbased on a single type of sensor, component, or a single type ofequipment in which a component is operating. Subsets for analysis may beselected or grouped based on common operating conditions such as size ofload, operational condition (e.g., intermittent or continuous),operating speed or tachometer output, common ambient environmentalconditions such as humidity, temperature, air or fluid particulate, andthe like. Subsets for analysis may be selected based on the effects ofother nearby equipment such as nearby machines rotating at similarfrequencies, nearby equipment producing electromagnetic fields, nearbyequipment producing heat, nearby equipment inducing movement orvibration, nearby equipment emitting vapors, chemicals or particulates,or other potentially interfering or intervening effects.

In embodiments, the monitoring application 8150 may analyze the selectedsubset. In an example, data from a single sensor may be analyzed overdifferent time periods such as one operating cycle, several operatingcycles, a month, a year, the life of the component, or the like. Datafrom multiple sensors of a common type measuring a common component typemay also be analyzed over different time periods. Trends in the datasuch as changing rates of change associated with start-up or differentpoints in the process may be identified. Correlation of trends andvalues for different sensors may be analyzed to identify thoseparameters whose short-term analysis might provide the best predictionregarding expected sensor performance. This information may betransmitted back to the monitoring device to update sensor models,sensor selection, sensor range, sensor scaling, sensor samplingfrequency, types of data collected, and the like, and be analyzedlocally or to influence the design of future monitoring devices.

In embodiments, the monitoring application 8150 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for a plurality ofsensors, operational history, historical detection values, sensor lifemodels, and the like for use analyzing the selected subset usingrule-based or model-based analysis. The monitoring application 8150 mayprovide recommendations regarding sensor selection, additional data tocollect, data to store with sensor data, and the like. The monitoringapplication 8150 may provide recommendations regarding schedulingrepairs and/or maintenance. The monitoring application 8150 may providerecommendations regarding replacing a sensor. The replacement sensor maymatch the sensor being replaced or the replacement sensor may have adifferent range, sensitivity, sampling frequency, and the like.

In embodiments, the monitoring application 8150 may include a remotelearning circuit structured to analyze sensor status data (e.g., sensoroverload or sensor failure) together with data from other sensors,failure data on components being monitored, equipment being monitored,output being produced, and the like. The remote learning system mayidentify correlations between sensor overload and data from othersensors.

An example monitoring system for data collection in an industrialenvironment includes a data acquisition circuit that interprets a numberof detection values, each of the detection values corresponding to inputreceived from at least one of a number of input sensors, a MUX havinginputs corresponding to a subset of the detection values, a MUX controlcircuit that interprets a subset of the number of detection values andprovides the logical control of the MUX and the correspondence of MUXinput and detected values as a result, where the logic control of theMUX includes adaptive scheduling of the select lines, a data analysiscircuit that receives an output from the MUX and data corresponding tothe logic control of the MUX resulting in a component health status, ananalysis response circuit that performs an operation in response to thecomponent health status, where the number of sensors includes at leasttwo sensors such as a temperature sensor, a load sensor, a vibrationsensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor,an accelerometer, a tri-axial vibration sensor, and/or a tachometer. Incertain further embodiments, an example system includes: where at leastone of the number of detection values may correspond to a fusion of twoor more input sensors representing a virtual sensor; where the systemfurther includes a data storage circuit that stores at least one ofcomponent specifications and anticipated component state information andbuffers a subset of the number of detection values for a predeterminedlength of time; where the system further includes a data storage circuitthat stores at least one of a component specification and anticipatedcomponent state information and buffers the output of the MUX and datacorresponding to the logic control of the MUX for a predetermined lengthof time; where the data analysis circuit includes a peak detectioncircuit, a phase detection circuit, a bandpass filter circuit, afrequency transformation circuit, a frequency analysis circuit, a PLLcircuit, a torsional analysis circuit, and/or a bearing analysiscircuit; where operation further includes storing additional data in thedata storage circuit; where the operation includes at least one ofenabling or disabling one or more portions of the MUX circuit; and/orwhere the operation includes causing the MUX control circuit to alterthe logical control of the MUX and the correspondence of MUX input anddetected values. In certain embodiments, the system includes at leasttwo multiplexers; control of the correspondence of the multiplexer inputand the detected values further includes controlling the connection ofthe output of a first multiplexer to an input of a second multiplexer;control of the correspondence of the multiplexer input and the detectedvalues further comprises powering down at least a portion of one of theat least two multiplexers; and/or control of the correspondence of MUXinput and detected values includes adaptive scheduling of the selectlines. In certain embodiments, a data response circuit analyzes thestream of data from one or both MUXes, and recommends an action inresponse to the analysis.

An example testing system includes the testing system in communicationwith a number of analog and digital input sensors, a monitoring deviceincluding a data acquisition circuit that interprets a number ofdetection values, each of the number of detection values correspondingto at least one of the input sensors, a MUX having inputs correspondingto a subset of the detection values, a MUX control circuit thatinterprets a subset of the number of detection values and provides thelogical control of the MUX and control of the correspondence of MUXinput and detected values as a result, where the logic control of theMUX includes adaptive scheduling of the select lines, and a userinterface enabled to accept scheduling input for select lines anddisplay output of MUX and select line data.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by looking at both the amplitude and phase or timing ofdata signals relative to related data signals, timers, reference signalsor data measurements. An embodiment of a data monitoring device 8500 isshown in FIG. 58 and may include a plurality of sensors 8506communicatively coupled to a controller 8502. The controller 8502 mayinclude a data acquisition circuit 8504, a signal evaluation circuit8508 and a response circuit 8510. The plurality of sensors 8506 may bewired to ports on the data acquisition circuit 8504 or wirelessly incommunication with the data acquisition circuit 8504. The plurality ofsensors 8506 may be wirelessly connected to the data acquisition circuit8504. The data acquisition circuit 8504 may be able to access detectionvalues corresponding to the output of at least one of the plurality ofsensors 8506 where the sensors 8506 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of sensors 8506 for a data monitoringdevice 8500 designed for a specific component or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, reliability of thesensors, and the like. The impact of failure may drive the extent towhich a component or piece of equipment is monitored with more sensorsand/or higher capability sensors being dedicated to systems whereunexpected or undetected failure would be costly or have severeconsequences.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors8506 may comprise one or more of, without limitation, a vibrationsensor, a thermometer, a hygrometer, a voltage sensor, a current sensor,an accelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition and/or spectral analysis,and/or object position or movement), an image sensor, a structured lightsensor, a laser-based image sensor, an acoustic wave sensor, adisplacement sensor, a turbidity meter, a viscosity meter, a loadsensor, a tri-axial sensor, an accelerometer, a tachometer, a fluidpressure meter, an air flow meter, a horsepower meter, a flow ratemeter, a fluid particle detector, an acoustical sensor, a pH sensor, andthe like, including, without limitation, any of the sensors describedthroughout this disclosure and the documents incorporated by reference.

The sensors 8506 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors8506 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 8506 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

In embodiments, as illustrated in FIG. 58, the sensors 8506 may be partof the data monitoring device 8500, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 59 and 60,sensors 8518, either new or previously attached to or integrated intothe equipment or component, may be opportunistically connected to oraccessed by a monitoring device 8512. The sensors 8518 may be directlyconnected to input ports 8520 on the data acquisition circuit 8516 of acontroller 8514 or may be accessed by the data acquisition circuit 8516wirelessly, such as by a reader, interrogator, or other wirelessconnection, such as over a short-distance wireless protocol. Inembodiments, a data acquisition circuit 8516 may access detection valuescorresponding to the sensors 8518 wirelessly or via a separate source orsome combination of these methods. In embodiments, the data acquisitioncircuit 8504 may include a wireless communications circuit 8522 able towirelessly receive data opportunistically from sensors 8518 in thevicinity and route the data to the input ports 8520 on the dataacquisition circuit 8516.

In an embodiment, as illustrated in FIGS. 61 and 62, the signalevaluation circuit 8508 may then process the detection values to obtaininformation about the component or piece of equipment being monitored.Information extracted by the signal evaluation circuit 8508 may compriserotational speed, vibrational data including amplitudes, frequencies,phase, and/or acoustical data, and/or non-phase sensor data such astemperature, humidity, image data, and the like.

The signal evaluation circuit 8508 may include one or more componentssuch as a phase detection circuit 8528 to determine a phase differencebetween two time-based signals, a phase lock loop circuit 8530 to adjustthe relative phase of a signal such that it is aligned with a secondsignal, timer or reference signal, and/or a band pass filter circuit8532 which may be used to separate out signals occurring at differentfrequencies. An example band pass filter circuit 8532 includes anyfiltering operations understood in the art, including at least alow-pass filter, a high-pass filter, and/or a band pass filter—forexample to exclude or reduce frequencies that are not of interest for aparticular determination, and/or to enhance the signal for frequenciesof interest. Additionally, or alternatively, a band pass filter circuit8532 includes one or more notch filters or other filtering mechanism tonarrow ranges of frequencies (e.g., frequencies from a known source ofnoise). This may be used to filter out dominant frequency signals suchas the overall rotation, and may help enable the evaluation of lowamplitude signals at frequencies associated with torsion, bearingfailure and the like.

In embodiments, understanding the relative differences may be enabled bya phase detection circuit 8528 to determine a phase difference betweentwo signals. It may be of value to understand a relative phase offset,if any, between signals such as when a periodic vibration occursrelative to a relative rotation of a piece of equipment. In embodiments,there may be value in understanding where in a cycle shaft vibrationsoccur relative to a motor control input to better balance the control ofthe motor. This may be particularly true for systems and components thatare operating at relative slow RPMs. Understanding of the phasedifference between two signals or between those signals and a timer mayenable establishing a relationship between a signal value and where itoccurs in a process or rotation. Understanding relative phasedifferences may help in evaluating the relationship between differentcomponents of a system such as in the creation of a vibrational modelfor an Operational Deflection Shape (ODS).

The signal evaluation circuit 8544 may perform frequency analysis usingtechniques such as a digital Fast Fourier transform (FFT), Laplacetransform, Z-transform, wavelet transform, other frequency domaintransform, or other digital or analog signal analysis techniques,including, without limitation, complex analysis, including complex phaseevolution analysis. An overall rotational speed or tachometer may bederived from data from sensors such as rotational velocity meters,accelerometers, displacement meters and the like. Additional frequenciesof interest may also be identified. These may include frequencies nearthe overall rotational speed as well as frequencies higher than that ofthe rotational speed. These may include frequencies that arenonsynchronous with an overall rotational speed. Signals observed atfrequencies that are multiples of the rotational speed may be due tobearing induced vibrations or other behaviors or situations involvingbearings. In some instances, these frequencies may be in the range ofone times the rotational speed, two times the rotational speed, threetimes the rotational speed, and the like, up to 3.15 to 15 times therotational speed, or higher. In some embodiments, the signal evaluationcircuit 8544 may select RC components for a band pass filter circuit8532 based on overall rotational speed to create a band pass filtercircuit 8532 to remove signals at expected frequencies such as theoverall rotational speed, to facilitate identification of smallamplitude signals at other frequencies. In embodiments, variablecomponents may be selected, such that adjustments may be made in keepingwith changes in the rotational speed, so that the band pass filter maybe a variable band pass filter. This may occur under control ofautomatically self-adjusting circuit elements, or under control of aprocessor, including automated control based on a model of the circuitbehavior, where a rotational speed indicator or other data is providedas a basis for control.

In embodiments, rather than performing frequency analysis, the signalevaluation circuit 8544 may utilize the time-based detection values toperform transitory signal analysis. These may include identifying abruptchanges in signal amplitude including changes where the change inamplitude exceeds a predetermined value or exists for a certainduration. In embodiments, the time-based sensor data may be aligned witha timer or reference signal allowing the time-based sensor data to bealigned with, for example, a time or location in a cycle. Additionalprocessing to look at frequency changes over time may include the use ofShort-Time Fourier Transforms (STFT) or a wavelet transform.

In embodiments, frequency-based techniques and time-based techniques maybe combined, such as using time-based techniques to determine discretetime periods during which given operational modes or states areoccurring and using frequency-based techniques to determine behaviorwithin one or more of the discrete time periods.

In embodiments, the signal evaluation circuit may utilize demodulationtechniques for signals obtained from equipment running at slow speedssuch as paper and pulp machines, mining equipment, and the like. Asignal evaluation circuit employing a demodulation technique maycomprise a band-pass filter circuit, a rectifier circuit, and/or a lowpass circuit prior to transforming the data to the frequency domain.

The response circuit 8510 8710 may further comprise evaluating theresults of the signal evaluation circuit 8508 8544 and, based on certaincriteria, initiating an action. Criteria may include a predeterminedmaximum or minimum value for a detection value from a specific sensor, avalue of a sensor's corresponding detection value over time, a change invalue, a rate of change in value, and/or an accumulated value (e.g., atime spent above/below a threshold value, a weighted time spentabove/below one or more threshold values, and/or an area of the detectedvalue above/below one or more threshold values). The criteria mayinclude a sensor's detection values at certain frequencies or phaseswhere the frequencies or phases may be based on the equipment geometry,equipment control schemes, system input, historical data, currentoperating conditions, and/or an anticipated response. The criteria maycomprise combinations of data from different sensors such as relativevalues, relative changes in value, relative rates of change in value,relative values over time, and the like. The relative criteria maychange with other data or information such as process stage, type ofproduct being processed, type of equipment, ambient temperature andhumidity, external vibrations from other equipment, and the like. Therelative criteria may include level of synchronicity with an overallrotational speed, such as to differentiate between vibration induced bybearings and vibrations resulting from the equipment design. Inembodiments, the criteria may be reflected in one or more calculatedstatistics or metrics (including ones generated by further calculationson multiple criteria or statistics), which in turn may be used forprocessing (such as on board a data collector or by an external system),such as to be provided as an input to one or more of the machinelearning capabilities described in this disclosure, to a control system(which may be an on-board data collector or remote, such as to controlselection of data inputs, multiplexing of sensor data, storage, or thelike), or as a data element that is an input to another system, such asa data stream or data package that may be available to a datamarketplace, a SCADA system, a remote control system, a maintenancesystem, an analytic system, or other system.

In an illustrative and non-limiting example, an alert may be issued ifthe vibrational amplitude and/or frequency exceeds a predeterminedmaximum value, if there is a change or rate of change that exceeds apredetermined acceptable range, and/or if an accumulated value based onvibrational amplitude and/or frequency exceeds a threshold. Certainembodiments are described herein as detected values exceeding thresholdsor predetermined values, but detected values may also fall belowthresholds or predetermined values—for example where an amount of changein the detected value is expected to occur, but detected values indicatethat the change may not have occurred. For example, and withoutlimitation, vibrational data may indicate system agitation levels,properly operating equipment, or the like, and vibrational data belowamplitude and/or frequency thresholds may be an indication of a processthat is not operating according to expectations. Except where thecontext clearly indicates otherwise, any description herein describing adetermination of a value above a threshold and/or exceeding apredetermined or expected value is understood to include determinationof a value below a threshold and/or falling below a predetermined orexpected value.

The predetermined acceptable range may be based on anticipated systemresponse or vibration based on the equipment geometry and control schemesuch as number of bearings, relative rotational speed, influx of powerto the system at a certain frequency, and the like. The predeterminedacceptable range may also be based on long term analysis of detectionvalues across a plurality of similar equipment and components andcorrelation of data with equipment failure. Based on vibration phaseinformation, a physical location of a problem may be identified. Basedon the vibration phase information system design flaws, off-nominaloperation, and/or component or process failures may be identified. Insome embodiments, an alert may be issued based on changes or rates ofchange in the data over time such as increasing amplitude or shifts inthe frequencies or phases at which a vibration occurs. In someembodiments, an alert may be issued based on accumulated values such astime spent over a threshold, weighted time spent over one or morethresholds, and/or an area of a curve of the detected value over one ormore thresholds. In embodiments, an alert may be issued based on acombination of data from different sensors such as relative changes invalue, or relative rates of change in amplitude, frequency of phase inaddition to values of non-phase sensors such as temperature, humidityand the like. For example, an increase in temperature and energy atcertain frequencies may indicate a hot bearing that is starting to fail.In embodiments, the relative criteria for an alarm may change with otherdata or information such as process stage, type of product beingprocessed on equipment, ambient temperature and humidity, externalvibrations from other equipment and the like.

In embodiments, response circuit 8510 may cause the data acquisitioncircuit 8504 to enable or disable the processing of detection valuescorresponding to certain sensors based on the some of the criteriadiscussed above. This may include switching to sensors having differentresponse rates, sensitivity, ranges, and the like; accessing new sensorsor types of sensors, and the like. Switching may be undertaken based ona model, a set of rules, or the like. In embodiments, switching may beunder control of a machine learning system, such that switching iscontrolled based on one or more metrics of success, combined with inputdata, over a set of trials, which may occur under supervision of a humansupervisor or under control of an automated system. Switching mayinvolve switching from one input port to another (such as to switch fromone sensor to another). Switching may involve altering the multiplexingof data, such as combining different streams under differentcircumstances. Switching may involve activating a system to obtainadditional data, such as moving a mobile system (such as a robotic ordrone system), to a location where different or additional data isavailable (such as positioning an image sensor for a different view orpositioning a sonar sensor for a different direction of collection) orto a location where different sensors can be accessed (such as moving acollector to connect up to a sensor that is disposed at a location in anenvironment by a wired or wireless connection). The response circuit8510 may make recommendations for the replacement of certain sensors inthe future with sensors having different response rates, sensitivity,ranges, and the like. The response circuit 8510 may recommend designalterations for future embodiments of the component, the piece ofequipment, the operating conditions, the process, and the like.

In embodiments, the response circuit 8510 may recommend maintenance atan upcoming process stop or initiate a maintenance call. The responsecircuit 8510 may recommend changes in process or operating parameters toremotely balance the piece of equipment. In embodiments, the responsecircuit 8510 may implement or recommend process changes—for example tolower the utilization of a component that is near a maintenanceinterval, operating off-nominally, or failed for purpose but still atleast partially operational, to change the operating speed of acomponent (such as to put it in a lower-demand mode), to initiateamelioration of an issue (such as to signal for additional lubricationof a roller bearing set, or to signal for an alignment process for asystem that is out of balance), and the like.

In embodiments, as shown in FIG. 63, the data monitoring device 8540 mayfurther comprise a data storage circuit 8542, memory, and the like. Thesignal evaluation circuit 8544 may periodically store certain detectionvalues to enable the tracking of component performance over time.

In embodiments, based on relevant operating conditions and/or failuremodes which may occur in as sensor values approach one or more criteria,the signal evaluation circuit 8544 may store data in the data storagecircuit 8542 based on the fit of data relative to one or more criteria,such as those described throughout this disclosure. Based on one sensorinput meeting or approaching specified criteria or range, the signalevaluation circuit 8544 may store additional data such as RPMs,component loads, temperatures, pressures, vibrations or other sensordata of the types described throughout this disclosure. The signalevaluation circuit 8544 may store data at a higher data rate for greatergranularity in future processing, the ability to reprocess at differentsampling rates, and/or to enable diagnosing or post-processing of systeminformation where operational data of interest is flagged, and the like.

In embodiments, as shown in FIG. 64, a data monitoring system 8546 maycomprise at least one data monitoring device 8548. The at least one datamonitoring device 8548 comprising sensors 8506, a controller 8550comprising a data acquisition circuit 8504, a signal evaluation circuit8538, a data storage circuit 8542, and a communications circuit 8552 toallow data and analysis to be transmitted to a monitoring application8556 on a remote server 8554. The signal evaluation circuit 8538 maycomprise at least one of a phase detection circuit 8528, a phase lockloop circuit 8530, and/or a band pass circuit 8532. The signalevaluation circuit 8538 may periodically share data with thecommunication circuit 8552 for transmittal to the remote server 8554 toenable the tracking of component and equipment performance over time andunder varying conditions by a monitoring application 8556. Becauserelevant operating conditions and/or failure modes may occur as sensorvalues approach one or more criteria, the signal evaluation circuit 8538may share data with the communication circuit 8552 for transmittal tothe remote server 8554 based on the fit of data relative to one or morecriteria. Based on one sensor input meeting or approaching specifiedcriteria or range, the signal evaluation circuit 8538 may shareadditional data such as RPMs, component loads, temperatures, pressures,vibrations, and the like for transmittal. The signal evaluation circuit8538 may share data at a higher data rate for transmittal to enablegreater granularity in processing on the remote server.

In embodiments, as illustrated in FIG. 65, a data collection system 8560may have a plurality of monitoring devices 8558 collecting data onmultiple components in a single piece of equipment, collecting data onthe same component across a plurality of pieces of equipment (both thesame and different types of equipment) in the same facility, as well ascollecting data from monitoring devices in multiple facilities. Amonitoring application on a remote server may receive and store the datacoming from a plurality of the various monitoring devices. Themonitoring application may then select subsets of data which may bejointly analyzed. Subsets of monitoring data may be selected based ondata from a single type of component or data from a single type ofequipment in which the component is operating. Monitoring data may beselected or grouped based on common operating conditions such as size ofload, operational condition (e.g., intermittent, continuous), operatingspeed or tachometer, common ambient environmental conditions such ashumidity, temperature, air or fluid particulate, and the like.Monitoring data may be selected based on the effects of other nearbyequipment, such as nearby machines rotating at similar frequencies,nearby equipment producing electromagnetic fields, nearby equipmentproducing heat, nearby equipment inducing movement or vibration, nearbyequipment emitting vapors, chemicals or particulates, or otherpotentially interfering or intervening effects.

The monitoring application may then analyze the selected data set. Forexample, data from a single component may be analyzed over differenttime periods such as one operating cycle, several operating cycles, amonth, a year, or the like. Data from multiple components of the sametype may also be analyzed over different time periods. Trends in thedata such as changes in frequency or amplitude may be correlated withfailure and maintenance records associated with the same component orpiece of equipment. Trends in the data such as changing rates of changeassociated with start-up or different points in the process may beidentified. Additional data may be introduced into the analysis such asoutput product quality, output quantity (such as per unit of time),indicated success or failure of a process, and the like. Correlation oftrends and values for different types of data may be analyzed toidentify those parameters whose short-term analysis might provide thebest prediction regarding expected performance. This information may betransmitted back to the monitoring device to update types of datacollected and analyzed locally or to influence the design of futuremonitoring devices.

In embodiments, information about the health of a component or piece ofindustrial equipment may be obtained by comparing the values of multiplesignals at the same point in a process. This may be accomplished byaligning a signal relative to other related data signals, timers, orreference signals. An embodiment of a data monitoring device 8700, 8718is shown in FIGS. 66-68 and may include a controller 8702, 8720. Thecontroller may include a data acquisition circuit 8704, 8722, a signalevaluation circuit 8708, a data storage circuit 8716 and an optionalresponse circuit 8710. The signal evaluation circuit 8708 may comprise atimer circuit 8714 and, optionally, a phase detection circuit 8712.

The data monitoring device may include a plurality of sensors 8706communicatively coupled to a controller 8702. The plurality of sensors8706 may be wired to ports on the data acquisition circuit 8704. Theplurality of sensors 8706 may be wirelessly connected to the dataacquisition circuit 8704 which may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors8706 where the sensors 8706 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.In embodiments, as illustrated in FIGS. 67 and 68, one or more externalsensors 8724 which are not explicitly part of a monitoring device 8718may be opportunistically connected to or accessed by the monitoringdevice 8718. The data acquisition circuit 8722 may include one or moreinput ports 8726. The one or more external sensors 8724 may be directlyconnected to the one or more input ports 8726 on the data acquisitioncircuit 8722 of the controller 8720. In embodiments, as shown in FIG.68, a data acquisition circuit 8722 may further comprise a wirelesscommunications circuit 8728 to access detection values corresponding tothe one or more external sensors 8724 wirelessly or via a separatesource or some combination of these methods.

The selection of the plurality of sensors 8706 8724 for connection to adata monitoring device 8700 8718 designed for a specific component orpiece of equipment may depend on a variety of considerations such asaccessibility for installing new sensors, incorporation of sensors inthe initial design, anticipated operational and failure conditions,resolution desired at various positions in a process or plant,reliability of the sensors, and the like. The impact of a failure, timeresponse of a failure (e.g., warning time and/or off-nominal modesoccurring before failure), likelihood of failure, and/or sensitivityrequired and/or difficulty to detect failed conditions may drive theextent to which a component or piece of equipment is monitored with moresensors and/or higher capability sensors being dedicated to systemswhere unexpected or undetected failure would be costly or have severeconsequences.

The signal evaluation circuit 8708 may process the detection values toobtain information about a component or piece of equipment beingmonitored. Information extracted by the signal evaluation circuit 8708may comprise information regarding what point or time in a processcorresponds with a detection value where the point in time is based on atiming signal generated by the timer circuit 8714. The start of thetiming signal may be generated by detecting an edge of a control signalsuch as a rising edge, falling edge or both where the control signal maybe associated with the start of a process. The start of the timingsignal may be triggered by an initial movement of a component or pieceof equipment. The start of the timing signal may be triggered by aninitial flow through a pipe or opening or by a flow achieving apredetermined rate. The start of the timing signal may be triggered by astate value indicating a process has commenced—for example the state ofa switch, button, data value provided to indicate the process hascommenced, or the like. Information extracted may comprise informationregarding a difference in phase, determined by the phase detectioncircuit 8712, between a stream of detection value and the time signalgenerated by the timer circuit 8714. Information extracted may compriseinformation regarding a difference in phase between one stream ofdetection values and a second stream of detection values where the firststream of detection values is used as a basis or trigger for a timingsignal generated by the timer circuit.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors8706 8724 may comprise one or more of, without limitation, athermometer, a hygrometer, a voltage sensor, a current sensor, anaccelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition and/or spectral analysis,and/or object position or movement), an image sensor, a displacementsensor, a turbidity meter, a viscosity meter, a load sensor, a tri-axialsensor, a tachometer, a fluid pressure meter, an air flow meter, ahorsepower meter, a flow rate meter, a fluid particle detector, anacoustical sensor, a pH sensor, and the like.

The sensors 8706 8724 may provide a stream of data over time that has aphase component, such as acceleration or vibration, allowing for theevaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors8706 8724 may provide a stream of data that is not phase based such astemperature, humidity, load, and the like. The sensors 8706 8724 mayprovide a continuous or near continuous stream of data over time,periodic readings, event-driven readings, and/or readings according to aselected interval or schedule.

In embodiments, as illustrated in FIGS. 69 and 70, the data acquisitioncircuit 8734 may further comprise a multiplexer circuit 8736 asdescribed elsewhere herein. Outputs from the multiplexer circuit 8736may be utilized by the signal evaluation circuit 8708. The responsecircuit 8710 may have the ability to turn on and off portions of themultiplexer circuit 8736. The response circuit 8710 may have the abilityto control the control channels of the multiplexer circuit 8736

The response circuit 8710 may further comprise evaluating the results ofthe signal evaluation circuit 8708 and, based on certain criteria,initiating an action. The criteria may include a sensor's detectionvalues at certain frequencies or phases relative to the timer signalwhere the frequencies or phases of interest may be based on theequipment geometry, equipment control schemes, system input, historicaldata, current operating conditions, and/or an anticipated response.Criteria may include a predetermined maximum or minimum value for adetection value from a specific sensor, a cumulative value of a sensor'scorresponding detection value over time, a change in value, a rate ofchange in value, and/or an accumulated value (e.g., a time spentabove/below a threshold value, a weighted time spent above/below one ormore threshold values, and/or an area of the detected value above/belowone or more threshold values). The criteria may comprise combinations ofdata from different sensors such as relative values, relative changes invalue, relative rates of change in value, relative values over time, andthe like. The relative criteria may change with other data orinformation such as process stage, type of product being processed, typeof equipment, ambient temperature and humidity, external vibrations fromother equipment, and the like.

Certain embodiments are described herein as detected values exceedingthresholds or predetermined values, but detected values may also fallbelow thresholds or predetermined values—for example where an amount ofchange in the detected value is expected to occur, but detected valuesindicate that the change may not have occurred. For example, and withoutlimitation, vibrational data may indicate system agitation levels,properly operating equipment, or the like, and vibrational data belowamplitude and/or frequency thresholds may be an indication of a processthat is not operating according to expectations. Except where thecontext clearly indicates otherwise, any description herein describing adetermination of a value above a threshold and/or exceeding apredetermined or expected value is understood to include determinationof a value below a threshold and/or falling below a predetermined orexpected value.

The predetermined acceptable range may be based on anticipated systemresponse or vibration based on the equipment geometry and control schemesuch as number of bearings, relative rotational speed, influx of powerto the system at a certain frequency, and the like. The predeterminedacceptable range may also be based on long term analysis of detectionvalues across a plurality of similar equipment and components andcorrelation of data with equipment failure.

In some embodiments, an alert may be issued based on the some of thecriteria discussed above. In an illustrative example, an increase intemperature and energy at certain frequencies may indicate a hot bearingthat is starting to fail. In embodiments, the relative criteria for analarm may change with other data or information such as process stage,type of product being processed on equipment, ambient temperature andhumidity, external vibrations from other equipment and the like. In anillustrative and non-limiting example, the response circuit 8710 mayinitiate an alert if a vibrational amplitude and/or frequency exceeds apredetermined maximum value, if there is a change or rate of change thatexceeds a predetermined acceptable range, and/or if an accumulated valuebased on vibrational amplitude and/or frequency exceeds a threshold.

In embodiments, response circuit 8710 may cause the data acquisitioncircuit 8704 to enable or disable the processing of detection valuescorresponding to certain sensors based on the some of the criteriadiscussed above. This may include switching to sensors having differentresponse rates, sensitivity, ranges, and the like; accessing new sensorsor types of sensors, and the like. This switching may be implemented bychanging the control signals for a multiplexer circuit 8736 and/or byturning on or off certain input sections of the multiplexer circuit8736. The response circuit 8710 may make recommendations for thereplacement of certain sensors in the future with sensors havingdifferent response rates, sensitivity, ranges, and the like. Theresponse circuit 8710 may recommend design alterations for futureembodiments of the component, the piece of equipment, the operatingconditions, the process, and the like.

In embodiments, the response circuit 8710 may recommend maintenance atan upcoming process stop or initiate a maintenance call. The responsecircuit 8710 may recommend changes in process or operating parameters toremotely balance the piece of equipment. In embodiments, the responsecircuit 8710 may implement or recommend process changes—for example tolower the utilization of a component that is near a maintenanceinterval, operating off-nominally, or failed for purpose but still atleast partially operational. In an illustrative example, vibration phaseinformation, derived by the phase detection circuit 8712 relative to atimer signal from the timer circuit 8714, may be indicative of aphysical location of a problem. Based on the vibration phaseinformation, system design flaws, off-nominal operation, and/orcomponent or process failures may be identified.

In embodiments, based on relevant operating conditions and/or failuremodes which may occur in as sensor values approach one or more criteria,the signal evaluation circuit 8708 may store data in the data storagecircuit 8716 based on the fit of data relative to one or more criteria.Based on one sensor input meeting or approaching specified criteria orrange, the signal evaluation circuit 8708 may store additional data suchas RPMs, component loads, temperatures, pressures, vibrations in thedata storage circuit 8716. The signal evaluation circuit 8708 may storedata at a higher data rate for greater granularity in future processing,the ability to reprocess at different sampling rates, and/or to enablediagnosing or post-processing of system information where operationaldata of interest is flagged, and the like.

An example monitoring system for data collection, includes a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors communicatively coupled to thedata acquisition circuit; a signal evaluation circuit comprising: atimer circuit structured to generate at least one timing signal; and aphase detection circuit structured to determine a relative phasedifference between at least one of the plurality of detection values andat least one of the timing signals from the timer circuit; and aresponse circuit structured to perform at least one operation inresponse to the relative phase difference. In certain furtherembodiments, an example system includes:

wherein the at least one operation is further in response to at leastone of: a change in amplitude of at least one of the plurality ofdetection values; a change in frequency or relative phase of at leastone of the plurality of detection values; a rate of change in bothamplitude and relative phase of at least one the plurality of detectionvalues; and a relative rate of change in amplitude and relative phase ofat least one the plurality of detection values; wherein the at least oneoperation comprises issuing an alert; wherein the alert may be one ofhaptic, audible and visual; a data storage circuit, wherein the relativephase difference and at least one of the detection values and the timingsignal are stored; wherein the at least one operation further comprisesstoring additional data in the data storage circuit; wherein the storingadditional data in the data storage circuit is further in response to atleast one of: a change in the relative phase difference and a relativerate of change in the relative phase difference; wherein the dataacquisition circuit further comprises at least one multiplexer circuit(MUX) whereby alternative combinations of detection values may beselected based on at least one of user input and a selected operatingparameter for a machine, wherein each of the plurality of detectionvalues corresponds to at least one of the input sensors; wherein the atleast one operation comprises enabling or disabling one or more portionsof the multiplexer circuit, or altering the multiplexer control lines;wherein the data acquisition circuit comprises at least two multiplexercircuits and the at least one operation comprises changing connectionsbetween the at least two multiplexer circuits; and/or the system furthercomprising a MUX control circuit structured to interpret a subset of theplurality of detection values and provide the logical control of the MUXand the correspondence of MUX input and detected values as a result,wherein the logic control of the MUX comprises adaptive scheduling ofthe select lines.

An example system for data collection, includes: a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a signal evaluation circuit comprising: a timercircuit structured to generate a timing signal based on a first detectedvalue of the plurality of detection values; and a phase detectioncircuit structured to determine a relative phase difference between asecond detection value of the plurality of detection values and thetiming signal; and a phase response circuit structured to perform atleast one operation in response to the phase difference. In certainfurther embodiments, an example system includes wherein the at least oneoperation is further in response to at least one of: a change inamplitude of at least one of the plurality of detection values; a changein frequency or relative phase of at least one of the plurality ofdetection values; a rate of change in both amplitude and relative phaseof at least one the plurality of detection values and a relative rate ofchange in amplitude and relative phase of at least one the plurality ofdetection values; wherein the at least one operation comprises issuingan alert; wherein the alert may be one of haptic, audible and visual;where the system, further includes a data storage circuit; wherein therelative phase difference and at least one of the detection values andthe timing signal are stored; wherein the at least one operation furtherincludes storing additional data in the data storage circuit; whereinthe storing additional data in the data storage circuit is further inresponse to at least one of: a change in the relative phase differenceand a relative rate of change in the relative phase difference; whereinthe data acquisition circuit further includes at least one multiplexer(MUX) circuit whereby alternative combinations of detection values maybe selected based on at least one of user input and a selected operatingparameter for a machine; wherein each of the plurality of detectionvalues corresponds to at least one of the input sensors; wherein the atleast one operation comprises enabling or disabling one or more portionsof the multiplexer circuit, or altering the multiplexer control lines;wherein the data acquisition circuit comprises at least two multiplexercircuits and the at least one operation comprises changing connectionsbetween the at least two multiplexer circuits; where the system furthercomprising a MUX control circuit structured to interpret a subset of theplurality of detection values and provide the logical control of the MUXand the correspondence of MUX input and detected values as a result;and/or wherein the logic control of the MUX comprises adaptivescheduling of the select lines.

An example system for data collection, processing, and utilization ofsignals in an industrial environment includes a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a signal evaluation circuit comprising: a timercircuit structured to generate a timing signal based on a first detectedvalue of the plurality of detection values; and a phase detectioncircuit structured to determine a relative phase difference between asecond detection value of the plurality of detection values and thetiming signal; a data storage facility for storing a subset of theplurality of detection values and the timing signal; a communicationcircuit structured to communicate at least one selected detection valueand the timing signal to a remote server; and a monitoring applicationon the remote server structured to receive the at least one selecteddetection value and the timing signal; jointly analyze a subset of thedetection values received from the plurality of monitoring devices; andrecommend an action. In certain embodiments, the example system furtherincludes wherein joint analysis comprises using the timing signal fromeach of the plurality of monitoring devices to align the detectionvalues from the plurality of monitoring devices and/or wherein thesubset of detection values is selected based on data associated with adetection value comprising at least one: common type of component,common type of equipment, and common operating conditions.

An example system for data collection in an industrial environment,includes: a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensorscommunicatively coupled to the data acquisition circuit, the dataacquisition circuit comprising a multiplexer circuit whereby alternativecombinations of the detection values may be selected based on at leastone of user input, a detected state and a selected operating parameterfor a machine, each of the plurality of detection values correspondingto at least one of the input sensors; a signal evaluation circuitcomprising: a timer circuit structured to generate a timing signal; anda phase detection circuit structured to determine a relative phasedifference between at least one of the plurality of detection values anda signal from the timer circuit; and a response circuit structured toperform at least one operation in response to the phase difference.

An example monitoring system for data collection in a piece ofequipment, includes a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensorscommunicatively coupled to the data acquisition circuit; a timer circuitstructured to generate a timing signal based on a first detected valueof the plurality of detection values; a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value comprising: a phase detection circuit structured todetermine a relative phase difference between a second detection valueof the plurality of detection values and the timing signal; and aresponse circuit structured to perform at least one operation inresponse to at the at least one of the vibration amplitude, vibrationfrequency and vibration phase location.

A monitoring system for bearing analysis in an industrial environment,the monitoring device includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a timercircuit structured to generate a timing signal a data storage forstoring specifications and anticipated state information for a pluralityof bearing types and buffering the plurality of detection values for apredetermined length of time; a timer circuit structured to generate atiming signal based on a first detected value of the plurality ofdetection values; a bearing analysis circuit structured to analyzebuffered detection values relative to specifications and anticipatedstate information resulting in a life prediction comprising: a phasedetection circuit structured to determine a relative phase differencebetween a second detection value of the plurality of detection valuesand the timing signal; a signal evaluation circuit structured to obtainat least one of vibration amplitude, vibration frequency and vibrationphase location corresponding to a second detected value: and a responsecircuit structured to perform at least one operation in response to atthe at least one of the vibration amplitude, vibration frequency andvibration phase location.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by monitoring the condition of various componentsthroughout a process. Monitoring may include monitoring the amplitude ofa sensor signal measuring attributes such as temperature, humidity,acceleration, displacement and the like. As in FIG. 230, an embodimentof a data monitoring device 9000 may include a plurality of sensors 9006communicatively coupled to a controller 9002. The controller 9002, whichmay be part of a data collection device, such as a mobile datacollector, or part of a system, such as a network-deployed orcloud-deployed system, may include a data acquisition circuit 9004, asignal evaluation circuit 9008 and a response circuit 9010. The signalevaluation circuit 9008 may comprise a peak detection circuit 9012.Additionally, the signal evaluation circuit 9008 may optionally compriseone or more of a phase detection circuit 9016, a bandpass filter circuit9018, a phase lock loop circuit, a torsional analysis circuit, a bearinganalysis circuit, and the like. The bandpass filter 9018 may be used tofilter a stream of detection values such that values, such as peaks andvalleys, are detected only at or within bands of interest, such asfrequencies of interest. The data acquisition circuit 9004 may includeone or more analog-to-digital converter circuits 9014. A peak amplitudedetected by the peak detection circuit 9012 may be input into one ormore analog-to-digital converter circuits 9014 to provide a referencevalue for scaling output of the analog-to-digital converter circuits9014 appropriately.

The plurality of sensors 9006 may be wired to ports on the dataacquisition circuit 9004. The plurality of sensors 9006 may bewirelessly connected to the data acquisition circuit 9004. The dataacquisition circuit 9004 may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors9006 where the sensors 9006 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of sensors 9006 for a data monitoringdevice 9000 designed for a specific component or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, resolution desired atvarious positions in a process or plant, reliability of the sensors,power availability, power utilization, storage utilization, and thelike. The impact of a failure, time response of a failure (e.g., warningtime and/or off-optimal modes occurring before failure), likelihood offailure, extent of impact of failure, and/or sensitivity required and/ordifficulty to detection failure conditions may drive the extent to whicha component or piece of equipment is monitored with more sensors and/orhigher capability sensors being dedicated to systems where unexpected orundetected failure would be costly or have severe consequences.

The signal evaluation circuit 9008 may process the detection values toobtain information about a component or piece of equipment beingmonitored. Information extracted by the signal evaluation circuit 9008may comprise information regarding a peak value of a signal such as apeak temperature, peak acceleration, peak velocity, peak pressure, peakweight bearing, peak strain, peak bending, or peak displacement. Thepeak detection may be done using analog or digital circuits. Inembodiments, the peak detection circuit 9012 may be able to distinguishbetween “local” or short term peaks in a stream of detection values anda “global” or longer term peak. In embodiments, the peak detectioncircuit 9012 may be able to identify peak shapes (not just a single peakvalue) such as flat tops, asymptotic approaches, discrete jumps in thepeak value or rapid/steep climbs in peak value, sinusoidal behaviorwithin ranges and the like. Flat topped peaks may indicate saturation atof a sensor. Asymptotic approaches to a peak may indicate linear systembehavior. Discrete jumps in value or steep changes in peak value mayindicate quantized or nonlinear behavior of either the sensor doing themeasurement or the behavior of the component. In embodiments, the systemmay be able to identify sinusoidal variations in the peak value withinan envelope, such as an envelope established by line or curve connectinga series of peak values. It should be noted that references to “peaks”should be understood to encompass one or more “valleys,” representing aseries of low points in measurement, except where context indicatesotherwise.

In embodiments, a peak value may be used as a reference for ananalog-to-digital conversion circuit 9014.

In an illustrative and non-limiting example, a temperature probe maymeasure the temperature of a gear as it rotates in a machine. The peaktemperature may be detected by a peak detection circuit 9012. The peaktemperature may be fed into an analog-to-digital converter circuit 9014to appropriately scale a stream of detection values corresponding totemperature readings of the gear as it rotates in a machine. The phaseof the stream of detection values corresponding to temperature relativeto an orientation of the gear may be determined by the phase detectioncircuit 9016. Knowing where in the rotation of the gear a peaktemperature is occurring may allow the identification of a bad geartooth.

In some embodiments, two or more sets of detection values may be fusedto create detection values for a virtual sensor. A peak detectioncircuit may be used to verify consistency in timing of peak valuesbetween at least one of the two or more sets of detection values and thedetection values for the virtual sensor.

In embodiments, the signal evaluation circuit 9008 may be able to resetthe peak detection circuit 9012 upon start-up of the monitoring device9000, upon edge detection of a control signal of the system beingmonitored, based on a user input, after a system error and the like. Inembodiments, the signal evaluation circuit 9008 may discard an initialportion of the output of the peak detection circuit 9012 prior to usingthe peak value as a reference value for an analog-to-digital conversioncircuit to allow the system to fully come on line.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors9006 may comprise one or more of, without limitation, a vibrationsensor, a thermometer, a hygrometer, a voltage sensor, a current sensor,an accelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition and/or spectral analysis,and/or object position or movement), an image sensor, a structured lightsensor, a laser-based image sensor, an acoustic wave sensor, adisplacement sensor, a turbidity meter, a viscosity meter, a loadsensor, a tri-axial sensor, an accelerometer, a tachometer, a fluidpressure meter, an air flow meter, a horsepower meter, a flow ratemeter, a fluid particle detector, an acoustical sensor, a pH sensor, andthe like, including, without limitation, any of the sensors describedthroughout this disclosure and the documents incorporated by reference.

The sensors 9006 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors9006 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 9006 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

In embodiments, as illustrated in FIG. 71, the sensors 9706 may be partof the data monitoring device 9700, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 72 and 73, oneor more external sensors 9724, which are not explicitly part of amonitoring device 9718 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to or accessed by the monitoring device 9718. The monitoringdevice may include a data acquisition circuit 9722, a signal evaluationcircuit 9708, a data storage circuit 9716 and a response circuit 9710.The signal evaluation circuit 9708 may comprise an overload detectioncircuit 9712, a sensor fault detection circuit 9714, or both.Additionally, the signal evaluation circuit 9708 may optionally compriseone or more of a peak detection circuit, a phase detection circuit, abandpass filter circuit, a frequency transformation circuit, a frequencyanalysis circuit, a phase lock loop circuit, a torsional analysiscircuit, a bearing analysis circuit, and the like. The data acquisitioncircuit 9722 may include one or more input ports 9726.

The one or more external sensors 9724 may be directly connected to theone or more input ports 9726 on the data acquisition circuit 9722 of thecontroller 9720 or may be accessed by the data acquisition circuit 9722wirelessly, such as by a reader, interrogator, or other wirelessconnection, such as over a short-distance wireless protocol. Inembodiments, as shown in FIG. 73, a data acquisition circuit 9722 mayfurther comprise a wireless communication circuit 9730. The dataacquisition circuit 9722 may use the wireless communication circuit 9730to access detection values corresponding to the one or more externalsensors 9724 wirelessly or via a separate source or some combination ofthese methods.

In embodiments, the data storage circuit 9716 may be structured to storesensor specifications, anticipated state information and detectedvalues. The data storage circuit 9716 may provide specifications andanticipated state information to the signal evaluation circuit 9708.

In embodiments, an overload detection circuit 9712 may detect sensoroverload by comparing the detected value associated with the sensor witha detected value associated with a sensor having a greater range/lowerresolution monitoring the same component/attribute. Inconsistencies inmeasured value may indicate that the higher resolution sensor may beoverloaded. In embodiments, an overload detection circuit 9712 maydetect sensor overload by evaluating consistency of sensor reading withreadings from other sensor data (monitoring the same or differentaspects of the component/piece of equipment. In embodiments, an overloaddetection circuit 9712 may detect sensor overload by evaluating datacollected by other sensors to identify conditions likely to result insensor overload (e.g., heat flux sensor data indicative of thelikelihood of overloading a sensor in a given location, accelerometerdata indicating a likelihood of overloading a velocity sensor, and thelike). In embodiments, an overload detection circuit 9712 may detectsensor overload by identifying flat line output following a risingtrend. In embodiments, an overload detection circuit 9712 may detectsensor overload by transforming the sensor data to frequency data, usingfor example a Fast Fourier Transform (FFT), and then looking for a“ski-jump” in the frequency data which may result from the data beingclipped due to an overloaded sensor. A sensor fault detection circuit9714 may identify failure of the sensor itself, sensor health, orpotential concerns regarding validity of sensor data. Rate of valuechange may be used to identify failure of the sensor itself. Forexample, a sudden jump to a maximum output may indicate a failure in thesensor rather than an overload of the sensor. In embodiments, anoverload detection circuit 9712 and/or a sensor fault detection circuit9712 may utilize sensor specifications, anticipated state information,sensor models and the like in the identification of sensor overload,failure, error, invalid data, and the like. In embodiments, the overloaddetection circuit 9712 or the sensor fault detection circuit 9714 mayuse detection values from other sensors and output from additionalcomponents such as a peak detection circuit and/or a phase detectioncircuit and/or a bandpass filter circuit and/or a frequencytransformation circuit and/or a frequency analysis circuit and/or aphase lock loop circuit and the like to identify potential sources forthe identified sensor overload, sensor faults, sensor failure, or thelike. Sources or factors involved in sensor overload may includelimitations on sensor range, sensor resolution, and sensor samplingfrequency. Sources of apparent sensor overload may be due to a range,resolution or sampling frequency of a multiplexor suppling detectionvalues associated with the sensor. Sources of factors involved inapparent sensor faults or failures may include environmental conditions;for example, excessive heat or cold may be associated with damage tosemiconductor-based sensors, which may result in erratic sensor data,failure of a sensor to produce data, data that appears out of the rangeof normal behavior (e.g., large, discrete jumps in temperature for asystem that does not normally experience such changes). Surges incurrent and/or voltage may be associated with damage to electricallyconnected sensors with sensitive components. Excessive vibration mayresult in physical damage to sensitive components of a sensor such aswires and/or connectors. An impact, which may be indicated by suddenacceleration or acoustical data may result in physical damage to asensor with sensitive components such as wires and/or connectors. Arapid increase in humidity in the environment surrounding a sensor or anabsence of oxygen may indicate water damage to a sensor. A suddenabsence of signal from a sensor may be indicative of sensordisconnection which may due to vibration, impact and the like. A sensorthat requires power may run out of battery power or be disconnected froma power source. In embodiments, the overload detection circuit 9712 orthe sensor fault detection circuit 9714 may output a sensor status wherethe sensor status may be one of sensor overload, sensor failure, sensorfault, sensor healthy, and the like. The sensor fault detection circuit9714 may determine one of a sensor fault status and a sensor validitystatus.

In embodiments, as illustrated in FIG. 74, the data acquisition circuit9722 may further comprise a multiplexer circuit 9731 as describedelsewhere herein. Outputs from the multiplexer circuit 9731 may beutilized by the signal evaluation circuit 9708. The response circuit9710 may have the ability to turn on or off portions of the multiplexorcircuit 9731. The response circuit 9710 may have the ability to controlthe control channels of the multiplexor circuit 9731.

In embodiments, the response circuit 9710 may initiate a variety ofactions based on the sensor status provided by the overload detectioncircuit 9712. The response circuit 9710 may continue using the sensor ifthe sensor status is “sensor healthy.” The response circuit 9710 mayadjust a sensor scaling value (e.g., from 100 mV/gram to 10 mV/gram).The response circuit 9710 may increase an acquisition range for analternate sensor. The response circuit 9710 may back sensor data out ofprevious calculations and evaluations such as bearing analysis,torsional analysis and the like. The response circuit 9710 may useprojected or anticipated data (based on data acquired prior tooverload/failure) in place of the actual sensor data for calculationsand evaluations such as bearing analysis, torsional analysis and thelike. The response circuit 9710 may issue an alarm. The response circuit9710 may issue an alert that may comprise notification that the sensoris out of range together with information regarding the extent of theoverload such as “overload range-data response may not be reliableand/or linear”, “destructive range-sensor may be damaged,” and the like.The response circuit 9710 may issue an alert where the alert maycomprise information regarding the effect of sensor load such as “unableto monitor machine health” due to sensor overload/failure,” and thelike.

In embodiments, the response circuit 9710 may cause the data acquisitioncircuit 9704 to enable or disable the processing of detection valuescorresponding to certain sensors based on the sensor statues describedabove. This may include switching to sensors having different responserates, sensitivity, ranges, and the like; accessing new sensors or typesof sensors, accessing data from multiple sensors, recruiting additionaldata collectors (such as routing the collectors to a point of work,using routing methods and systems disclosed throughout this disclosureand the documents incorporated by reference) and the like. Switching maybe undertaken based on a model, a set of rules, or the like. Inembodiments, switching may be under control of a machine learningsystem, such that switching is controlled based on one or more metricsof success, combined with input data, over a set of trials, which mayoccur under supervision of a human supervisor or under control of anautomated system. Switching may involve switching from one input port toanother (such as to switch from one sensor to another). Switching mayinvolve altering the multiplexing of data, such as combining differentstreams under different circumstances. Switching may involve activatinga system to obtain additional data, such as moving a mobile system (suchas a robotic or drone system), to a location where different oradditional data is available (such as positioning an image sensor for adifferent view or positioning a sonar sensor for a different directionof collection) or to a location where different sensors can be accessed(such as moving a collector to connect up to a sensor that is disposedat a location in an environment by a wired or wireless connection). Thisswitching may be implemented by changing the control signals for amultiplexor circuit 9731 and/or by turning on or off certain inputsections of the multiplexor circuit 9731.

In embodiments, the response circuit 9710 may make recommendations forthe replacement of certain sensors in the future with sensors havingdifferent response rates, sensitivity, ranges, and the like. Theresponse circuit 9710 may recommend design alterations for futureembodiments of the component, the piece of equipment, the operatingconditions, the process, and the like.

In embodiments, the response circuit 9710 may recommend maintenance atan upcoming process stop or initiate a maintenance call where themaintenance may include the replacement of the sensor with the same oran alternate type of sensor having a different response rate,sensitivity, range and the like. In embodiments, the response circuit9710 may implement or recommend process changes—for example to lower theutilization of a component that is near a maintenance interval,operating off-nominally, or failed for purpose but still at leastpartially operational, to change the operating speed of a component(such as to put it in a lower-demand mode), to initiate amelioration ofan issue (such as to signal for additional lubrication of a rollerbearing set, or to signal for an alignment process for a system that isout of balance), and the like.

In embodiments, the signal evaluation circuit 9708 and/or the responsecircuit 9710 may periodically store certain detection values in the datastorage circuit 9716 to enable the tracking of component performanceover time. In embodiments, based on sensor status, as describedelsewhere herein recently measured sensor data and related operatingconditions such as RPMs, component loads, temperatures, pressures,vibrations or other sensor data of the types described throughout thisdisclosure in the data storage circuit 9716 to enable the backing out ofoverloaded/failed sensor data. The signal evaluation circuit 9708 maystore data at a higher data rate for greater granularity in futureprocessing, the ability to reprocess at different sampling rates, and/orto enable diagnosing or post-processing of system information whereoperational data of interest is flagged, and the like.

In embodiments as shown in FIGS. 75, 76, 77, and 78, a data monitoringsystem 9726 may include at least one data monitoring device 9728. Atleast one data monitoring device 9728 may include sensors 9706 and acontroller 9730 comprising a data acquisition circuit 9704, a signalevaluation circuit 9708, a data storage circuit 9716, and acommunication circuit 9754 to allow data and analysis to be transmittedto a monitoring application 9736 on a remote server 9734. The signalevaluation circuit 9708 may include at least an overload detectioncircuit 9712. The signal evaluation circuit 9708 may periodically sharedata with the communication circuit 9732 for transmittal to the remoteserver 9734 to enable the tracking of component and equipmentperformance over time and under varying conditions by a monitoringapplication 9736. Based on the sensor status, the signal evaluationcircuit 9708 and/or response circuit 9710 may share data with thecommunication circuit 9732 for transmittal to the remote server 9734based on the fit of data relative to one or more criteria. Data mayinclude recent sensor data and additional data such as RPMs, componentloads, temperatures, pressures, vibrations, and the like fortransmittal. The signal evaluation circuit 9708 may share data at ahigher data rate for transmittal to enable greater granularity inprocessing on the remote server.

In embodiments, as shown in FIG. 75, the communication circuit 9732 maycommunicate data directly to a remote server 9734. In embodiments asshown in FIG. 76, the communication circuit 9732 may communicate data toan intermediate computer 9738 which may include a processor 9740 runningan operating system 9742 and a data storage circuit 9744.

In embodiments, as illustrated in FIGS. 77 and 78, a data collectionsystem 9746 may have a plurality of monitoring devices 9728 collectingdata on multiple components in a single piece of equipment, collectingdata on the same component across a plurality of pieces of equipment,(both the same and different types of equipment) in the same facility aswell as collecting data from monitoring devices in multiple facilities.A monitoring application 9736 on a remote server 9734 may receive andstore one or more of detection values, timing signals and data comingfrom a plurality of the various monitoring devices 9728.

In embodiments, as shown in FIG. 77, the communication circuit 9732 maycommunicated data directly to a remote server 9734. In embodiments, asshown in FIG. 78, the communication circuit 9732 may communicate data toan intermediate computer 9738 which may include a processor 9740 runningan operating system 9742 and a data storage circuit 9744. There may bean individual intermediate computer 9738 associated with each monitoringdevice 9728 or an individual intermediate computer 9738 may beassociated with a plurality of monitoring devices 9728 where theintermediate computer 9738 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server9734. Communication to the remote server 9734 may be streaming, batch(e.g., when a connection is available) or opportunistic.

The monitoring application 9736 may select subsets of the detectionvalues to be jointly analyzed. Subsets for analysis may be selectedbased on a single type of sensor, component or a single type ofequipment in which a component is operating. Subsets for analysis may beselected or grouped based on common operating conditions such as size ofload, operational condition (e.g., intermittent, continuous), operatingspeed or tachometer, common ambient environmental conditions such ashumidity, temperature, air or fluid particulate, and the like. Subsetsfor analysis may be selected based on the effects of other nearbyequipment such as nearby machines rotating at similar frequencies,nearby equipment producing electromagnetic fields, nearby equipmentproducing heat, nearby equipment inducing movement or vibration, nearbyequipment emitting vapors, chemicals or particulates, or otherpotentially interfering or intervening effects.

In embodiments, the monitoring application 9736 may analyze the selectedsubset. In an illustrative example, data from a single sensor may beanalyzed over different time periods such as one operating cycle,several operating cycles, a month, a year, the life of the component orthe like. Data from multiple sensors of a common type measuring a commoncomponent type may also be analyzed over different time periods. Trendsin the data such as changing rates of change associated with start-up ordifferent points in the process may be identified. Correlation of trendsand values for different sensors may be analyzed to identify thoseparameters whose short-term analysis might provide the best predictionregarding expected sensor performance. This information may betransmitted back to the monitoring device to update sensor models,sensor selection, sensor range, sensor scaling, sensor samplingfrequency, types of data collected and analyzed locally or to influencethe design of future monitoring devices.

In embodiments, the monitoring application 9736 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for a plurality ofsensors, operational history, historical detection values, sensor lifemodels and the like for use analyzing the selected subset usingrule-based or model-based analysis. The monitoring application 9736 mayprovide recommendations regarding sensor selection, additional data tocollect, or data to store with sensor data. The monitoring application9736 may provide recommendations regarding scheduling repairs and/ormaintenance. The monitoring application 9736 may provide recommendationsregarding replacing a sensor. The replacement sensor may match thesensor being replaced or the replacement sensor may have a differentrange, sensitivity, sampling frequency and the like.

In embodiments, the monitoring application 9736 may include a remotelearning circuit structured to analyze sensor status data (e.g., sensoroverload, sensor faults, sensor failure) together with data from othersensors, failure data on components being monitored, equipment beingmonitored, product being produced, and the like. The remote learningsystem may identify correlations between sensor overload and data fromother sensors.

Clause 1: In embodiments, a monitoring system for data collection in anindustrial environment, the monitoring system comprising: a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors; a data storage circuitstructured to store sensor specifications, anticipated state informationand detected values; a signal evaluation circuit comprising: an overloadidentification circuit structured to determine a sensor overload statusof at least one sensor in response to the plurality of detection valuesand at least one of anticipated state information and sensorspecification; a sensor fault detection circuit structured to determineone of a sensor fault status and a sensor validity status of at leastone sensor in response to the plurality of detection values and at leastone of anticipated state information and sensor specification; and aresponse circuit structured to perform at least one operation inresponse to one of a sensor overload status, a sensor health status, anda sensor validity status. A monitoring system of clause 1, the systemfurther comprising a mobile data collector for collecting data from theplurality of input sensors. 3. The monitoring system of clause 1,wherein the at least one operation comprises issuing an alert or analarm. 4. The monitoring system of clause 1, wherein the at least oneoperation further comprises storing additional data in the data storagecircuit. 5. The monitoring system of clause 1, the system furthercomprising a multiplexor (MUX) circuit.6. The monitoring system ofclause 5, wherein the at least one operation comprises at least one ofenabling or disabling one or more portions of the multiplexer circuitand altering the multiplexer control lines. 7. The monitoring system ofclause 5, the system further comprising at least two multiplexer (MUX)circuits and the at least one operation comprises changing connectionsbetween the at least two multiplexer circuits. 8. The monitoring systemof clause 7, the system further comprising a MUX control circuitstructured to interpret a subset of the plurality of detection valuesand provide the logical control of the MUX and the correspondence of MUXinput and detected values as a result, wherein the logic control of theMUX comprises adaptive scheduling of the multiplexer control lines. 9. Asystem for data collection, processing, and component analysis in anindustrial environment comprising: a plurality of monitoring devices,each monitoring device comprising: a data acquisition circuit structuredto interpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors; a data storage for storing specifications and anticipated stateinformation for a plurality of sensor types and buffering the pluralityof detection values for a predetermined length of time; a signalevaluation circuit comprising: an overload identification circuitstructured to determine a sensor overload status of at least one sensorin response to the plurality of detection values and at least one ofanticipated state information and sensor specification; a sensor faultdetection circuit structured to determine one of a sensor fault statusand a sensor validity status of at least one sensor in response to theplurality of detection values and at least one of anticipated stateinformation and sensor specification; and a response circuit structuredto perform at least one operation in response to one of a sensoroverload status, a sensor health status, and a sensor validity status; acommunication circuit structured to communicate with a remote serverproviding one of the sensor overload status, the sensor health status,and the sensor validity status and a portion of the buffered detectionvalues to the remote server; and a monitoring application on the remoteserver structured to: receive the at least one selected detection valueand one of the sensor overload status, the sensor health status, and thesensor validity status; jointly analyze a subset of the detection valuesreceived from the plurality of monitoring devices; and recommend anaction. 10. The system of clause 9, with at least one of the monitoringdevices further comprising a mobile data collector for collecting datafrom the plurality of input sensors. 11. The system of clause 9, whereinthe at least one operation comprises issuing an alert or an alarm. 12.The monitoring system of clause 9, wherein the at least one operationfurther comprises storing additional data in the data storage circuit.13. The system of clause 9, with at least one of the monitoring devicesfurther comprising further comprising a multiplexor (MUX) circuit. 14.The system of clause 13, wherein the at least one operation comprises atleast one of enabling or disabling one or more portions of themultiplexer circuit and altering the multiplexer control lines. 15. Thesystem of clause 9, at least one of the monitoring devices furthercomprising at least two multiplexer (MUX) circuits and the at least oneoperation comprises changing connections between the at least twomultiplexer circuits. 16. The monitoring system of clause 15, the systemfurther comprising a MUX control circuit structured to interpret asubset of the plurality of detection values and provide the logicalcontrol of the MUX and the correspondence of MUX input and detectedvalues as a result, wherein the logic control of the MUX comprisesadaptive scheduling of the multiplexer control lines. 17. The system ofclause 9, wherein the monitoring application comprises a remote learningcircuit structured to analyze sensor status data together sensor dataand identify correlations between sensor overload and data from othersystems. 18. The system of clause 9, the monitoring applicationstructured to subset detection values based on one of the sensoroverload status, the sensor health status, the sensor validity status,the anticipated life of a sensor associated with detection values, theanticipated type of the equipment associated with detection values, andoperational conditions under which detection values were measured. 19.The system of clause 9, wherein the supplemental information comprisesone of sensor specification, sensor historic performance, maintenancerecords, repair records and an anticipated state model. 20. The systemof clause 19, wherein the analysis of the subset of detection valuescomprises feeding a neural net with the subset of detection values andsupplemental information to learn to recognize various sensor operatingstates, health states, life expectancies and fault states utilizing deeplearning techniques.

Referring to FIGS. 79 through 106, embodiments of the presentdisclosure, including those involving expert systems, self-organization,machine learning, artificial intelligence, and the like, may benefitfrom the use of a neural net, such as a neural net trained for patternrecognition, for classification of one or more parameters,characteristics, or phenomena, for support of autonomous control, andother purposes. References to a neural net throughout this disclosureshould be understood to encompass a wide range of different types ofneural networks, machine learning systems, artificial intelligencesystems, and the like, such as feed forward neural networks, radialbasis function neural networks, self-organizing neural networks (e.g.,Kohonen self-organizing neural networks), recurrent neural networks,modular neural networks, artificial neural networks, physical neuralnetworks, multi-layered neural networks, convolutional neural networks,hybrids of neural networks with other expert systems (e.g., hybrid fuzzylogic—neural network systems), autoencoder neural networks,probabilistic neural networks, time delay neural networks, convolutionalneural networks, regulatory feedback neural networks, radial basisfunction neural networks, recurrent neural networks, Hopfield neuralnetworks, Boltzmann machine neural networks, self-organizing map (SOM)neural networks, learning vector quantization (LVQ) neural networks,fully recurrent neural networks, simple recurrent neural networks, echostate neural networks, long short-term memory neural networks,bi-directional neural networks, hierarchical neural networks, stochasticneural networks, genetic scale RNN neural networks, committee ofmachines neural networks, associative neural networks, physical neuralnetworks, instantaneously trained neural networks, spiking neuralnetworks, neocognition neural networks, dynamic neural networks,cascading neural networks, neuro-fuzzy neural networks, compositionalpattern-producing neural networks, memory neural networks, hierarchicaltemporal memory neural networks, deep feed forward neural networks,gated recurrent unit (GCU) neural networks, auto encoder neuralnetworks, variational auto encoder neural networks, de-noising autoencoder neural networks, sparse auto-encoder neural networks, Markovchain neural networks, restricted Boltzmann machine neural networks,deep belief neural networks, deep convolutional neural networks,deconvolutional neural networks, deep convolutional inverse graphicsneural networks, generative adversarial neural networks, liquid statemachine neural networks, extreme learning machine neural networks, echostate neural networks, deep residual neural networks, support vectormachine neural networks, neural Turing machine neural networks, and/orholographic associative memory neural networks, or hybrids orcombinations of the foregoing, or combinations with other expertsystems, such as rule-based systems, model-based systems (including onesbased on physical models, statistical models, flow-based models,biological models, biomimetic models, and the like).

In embodiments, the foregoing neural network may be configured toconnect with a DAQ instrument and other data collectors that may receiveanalog signals from one or more sensors. The foregoing neural networksmay also be configured to interface with, connect to, or integrate withexpert systems that can be local and/or available through one or morecloud networks. In embodiments, FIGS. 80 through 106 depict exemplaryneural networks and FIG. 79 depicts a legend showing the variouscomponents of the neural networks depicted throughout FIGS. 80 to 106.FIG. 79 depicts the various neural net components 10000, as depicted incells 10002 for which there are assigned functions and requirements. Inembodiments, the various neural net examples may include back feddata/sensor cells 10010, data/sensor cells 10012, noisy input cells,10014, and hidden cells, 10018. The neural net components 10000 alsoinclude the other following cells 10002: probabilistic hidden cells10020, spiking hidden cells 10022, output cells 10024, matchinput/output cell 10028, recurrent cell 10030, memory cell, 10032,different memory cell 10034, kernals 10038 and convolution or pool cells10040.

In FIG. 80, a streaming data collection system 10050 may include a DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including sensor 10060, sensor 10062 and sensor 10064. Thestreaming data collection system 10050 may include a perceptron neuralnetwork 10070 that may connect to, integrate with, or interface with anexpert system 10080. In FIG. 81, a streaming data collection system10090 may include the DAQ instrument 10052 or other data collectors thatmay gather analog signals from sensors including the sensors 10060,10062, 10064. The streaming data collection system 10090 may include afeed forward neural network 10092 that may connect to, integrate with,or interface with the expert system 10080. In FIG. 82, a streaming datacollection system 10100 may include the DAQ instrument 10052 or otherdata collectors that may gather analog signals from sensors includingthe sensors 10060, 10062, 10064. The streaming data collection system10100 may include a radial basis neural network 10102 that may connectto, integrate with, or interface with the expert system 10080. In FIG.83, a streaming data collection system 10110 may include the DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including the sensors 10060, 10062, 10064. The streamingdata collection system 10110 may include a deep feed forward neuralnetwork 10112 that may connect to, integrate with, or interface with theexpert system 10080. In FIG. 84, a streaming data collection system10120 may include the DAQ instrument 10052 or other data collectors thatmay gather analog signals from sensors including the sensors 10060,10062, 10064. The streaming data collection system 10120 may include arecurrent neural network 10122 that may connect to, integrate with, orinterface with the expert system 10080.

In FIG. 85, a streaming data collection system 10130 may include the DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including the sensors 10060, 10062, 10064. The streamingdata collection system 10130 may include a long/short term neuralnetwork 10132 that may connect to, integrate with, or interface with theexpert system 10080. In FIG. 86, a streaming data collection system10140 may include the DAQ instrument 10052 or other data collectors thatmay gather analog signals from sensors including the sensors 10060,10062, 10064. The streaming data collection system 10140 may include agated recurrent neural network 10142 that may connect to, integratewith, or interface with the expert system 10080. In FIG. 87, a streamingdata collection system 10150 may include the DAQ instrument 10052 orother data collectors that may gather analog signals from sensorsincluding the sensors 10060, 10062, 10064. The streaming data collectionsystem 10150 may include an auto encoder neural network 10152 that mayconnect to, integrate with, or interface with the expert system 10080.In FIG. 88, a streaming data collection system 10160 may include the DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including the sensors 10060, 10062, 10064. The streamingdata collection system 10160 may include a variational neural network10162 that may connect to, integrate with, or interface with the expertsystem 10080. In FIG. 89, a streaming data collection system 10170 mayinclude the DAQ instrument 10052 or other data collectors that maygather analog signals from sensors including the sensors 10060, 10062,10064. The streaming data collection system 10170 may include adenoising neural network 10172 that may connect to, integrate with, orinterface with the expert system 10080. In FIG. 90, a streaming datacollection system 10180 may include the DAQ instrument 10052 or otherdata collectors that may gather analog signals from sensors includingthe sensors 10060, 10062, 10064. The streaming data collection system10180 may include a sparse neural network 10182 that may connect to,integrate with, or interface with the expert system 10080. In FIG. 91, astreaming data collection system 10190 may include the DAQ instrument10052 or other data collectors that may gather analog signals fromsensors including the sensors 10060, 10062, 10064. The streaming datacollection system 10190 may include a Markov chain neural network 10182that may connect to, integrate with, or interface with the expert system10080.

In FIG. 92, a streaming data collection system 10200 may include the DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including the sensors 10060, 10062, 10064. The streamingdata collection system 10200 may include a Hopfield network neuralnetwork 10202 that may connect to, integrate with, or interface with theexpert system 10080. In FIG. 93, a streaming data collection system10210 may include the DAQ instrument 10052 or other data collectors thatmay gather analog signals from sensors including the sensors 10060,10062, 10064. The streaming data collection system 10210 may include aBoltzmann machine neural network 10212 that may connect to, integratewith, or interface with the expert system 10080. In FIG. 94, a streamingdata collection system 10220 may include the DAQ instrument 10052 orother data collectors that may gather analog signals from sensorsincluding the sensors 10060, 10062, 10064. The streaming data collectionsystem 10220 may include a restricted BM neural network 10222 that mayconnect to, integrate with, or interface with the expert system 10080.In FIG. 95, a streaming data collection system 10230 may include the DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including the sensors 10060, 10062, 10064. The streamingdata collection system 10230 may include a deep belief neural network10232 that may connect to, integrate with, or interface with the expertsystem 10080. In FIG. 96, a streaming data collection system 10240 mayinclude the DAQ instrument 10052 or other data collectors that maygather analog signals from sensors including the sensors 10060, 10062,10064. The streaming data collection system 10240 may include a deepconvolutional neural network 10242 that may connect to, integrate with,or interface with the expert system 10080. In FIG. 97, a streaming datacollection system 10250 may include the DAQ instrument 10052 or otherdata collectors that may gather analog signals from sensors includingthe sensors 10060, 10062, 10064. The streaming data collection system10250 may include a deconvolutional neural network 10242 that mayconnect to, integrate with, or interface with the expert system 10080.In FIG. 98, a streaming data collection system 10260 may include the DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including the sensors 10060, 10062, 10064. The streamingdata collection system 10260 may include a deep convolutional inversegraphics neural network 10262 that may connect to, integrate with, orinterface with the expert system 10080. In FIG. 99, a streaming datacollection system 10270 may include the DAQ instrument 10052 or otherdata collectors that may gather analog signals from sensors includingthe sensors 10060, 10062, 10064. The streaming data collection system10270 may include a generative adversarial neural network 10272 that mayconnect to, integrate with, or interface with the expert system 10080.In FIG. 90, a streaming data collection system 10280 may include the DAQinstrument 10052 or other data collectors that may gather analog signalsfrom sensors including the sensors 10060, 10062, 10064. The streamingdata collection system 10280 may include a liquid state machine neuralnetwork 10282 that may connect to, integrate with, or interface with theexpert system 10080. In FIG. 101, a streaming data collection system10290 may include the DAQ instrument 10052 or other data collectors thatmay gather analog signals from sensors including the sensors 10060,10062, 10064. The streaming data collection system 10290 may include anextreme learning machine neural network 10292 that may connect to,integrate with, or interface with the expert system 10080. In FIG. 102,a streaming data collection system 10300 may include the DAQ instrument10052 or other data collectors that may gather analog signals fromsensors including the sensors 10060, 10062, 10064. The streaming datacollection system 10300 may include an echo state neural network 10302that may connect to, integrate with, or interface with the expert system10080. In FIG. 103, a streaming data collection system 10310 may includethe DAQ instrument 10052 or other data collectors that may gather analogsignals from sensors including the sensors 10060, 10062, 10064. Thestreaming data collection system 10310 may include a deep residualneural network 10312 that may connect to, integrate with, or interfacewith the expert system 10080. In FIG. 104, a streaming data collectionsystem 10320 may include the DAQ instrument 10052 or other datacollectors that may gather analog signals from sensors including thesensors 10060, 10062, 10064. The streaming data collection system 10320may include a Kohonen neural network 10322 that may connect to,integrate with, or interface with the expert system 10080. In FIG. 105,a streaming data collection system 10330 may include the DAQ instrument10052 or other data collectors that may gather analog signals fromsensors including the sensors 10060, 10062, 10064. The streaming datacollection system 10330 may include a support vector machine neuralnetwork 10332 that may connect to, integrate with, or interface with theexpert system 10080. In FIG. 106, a streaming data collection system10340 may include the DAQ instrument 10052 or other data collectors thatmay gather analog signals from sensors including the sensors 10060,10062, 10064. The streaming data collection system 10340 may include aneural Turing machine neural network 10342 that may connect to,integrate with, or interface with the expert system 10080.

The foregoing neural networks may have a variety of nodes or neurons,which may perform a variety of functions on inputs, such as inputsreceived from sensors or other data sources, including other nodes.Functions may involve weights, features, feature vectors, and the like.Neurons may include perceptrons, neurons that mimic biological functions(such as of the human senses of touch, vision, taste, hearing, andsmell), and the like. Continuous neurons, such as with sigmoidalactivation, may be used in the context of various forms of neural net,such as where back propagation is involved.

In many embodiments, an expert system or neural network may be trained,such as by a human operator or supervisor, or based on a data set,model, or the like. Training may include presenting the neural networkwith one or more training data sets that represent values, such assensor data, event data, parameter data, and other types of data(including the many types described throughout this disclosure), as wellas one or more indicators of an outcome, such as an outcome of aprocess, an outcome of a calculation, an outcome of an event, an outcomeof an activity, or the like. Training may include training inoptimization, such as training a neural network to optimize one or moresystems based on one or more optimization approaches, such as Bayesianapproaches, parametric Bayes classifier approaches, k-nearest-neighborclassifier approaches, iterative approaches, interpolation approaches,Pareto optimization approaches, algorithmic approaches, and the like.Feedback may be provided in a process of variation and selection, suchas with a genetic algorithm that evolves one or more solutions based onfeedback through a series of rounds.

In embodiments, a plurality of neural networks may be deployed in acloud platform that receives data streams and other inputs collected(such as by mobile data collectors) in one or more industrialenvironments and transmitted to the cloud platform over one or morenetworks, including using network coding to provide efficienttransmission. In the cloud platform, optionally using massively parallelcomputational capability, a plurality of different neural networks ofseveral types (including modular forms, structure-adaptive forms,hybrids, and the like) may be used to undertake prediction,classification, control functions, and provide other outputs asdescribed in connection with expert systems disclosed throughout thisdisclosure. The different neural networks may be structured to competewith each other (optionally including the use of evolutionaryalgorithms, genetic algorithms, or the like), such that an appropriatetype of neural network, with appropriate input sets, weights, node typesand functions, and the like, may be selected, such as by an expertsystem, for a specific task involved in a given context, workflow,environment process, system, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feed forwardneural network, which moves information in one direction, such as from adata input, like an analog sensor located on or proximal to anindustrial machine, through a series of neurons or nodes, to an output.Data may move from the input nodes to the output nodes, optionallypassing through one or more hidden nodes, without loops. In embodiments,feedforward neural networks may be constructed with various types ofunits, such as binary McCulloch-Pitts neurons, the simplest of which isa perceptron.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, which may be preferred in some situationsinvolving interpolation in a multi-dimensional space (such as whereinterpolation is helpful in optimizing a multi-dimensional function,such as for optimizing a data marketplace as described here, optimizingthe efficiency or output of a power generation system, a factory system,or the like, or other situation involving multiple dimensions). Inembodiments, each neuron in the RBF neural network stores an examplefrom a training set as a “prototype.” Linearity involved in thefunctioning of this neural network offers RBF the advantage of nottypically suffering from problems with local minima or maxima.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a radial basisfunction (RBF) neural network, such as one that employs a distancecriterion with respect to a center (e.g., a Gaussian function). A radialbasis function may be applied as a replacement for a hidden layer (suchas a sigmoidal hidden layer transfer) in a multi-layer perceptron. AnRBF network may have two layers, such as the case where an input ismapped onto each RBF in a hidden layer. In embodiments, an output layermay comprise a linear combination of hidden layer values representing,for example, a mean predicted output. The output layer value may providean output that is the same as or similar to that of a regression modelin statistics. In classification problems, the output layer may be asigmoid function of a linear combination of hidden layer values,representing a posterior probability. Performance in both cases is oftenimproved by shrinkage techniques, such as ridge regression in classicalstatistics. This corresponds to a prior belief in small parameter values(and therefore smooth output functions) in a Bayesian framework. RBFnetworks may avoid local minima, because the only parameters that areadjusted in the learning process are the linear mapping from hiddenlayer to output layer. Linearity ensures that the error surface isquadratic and therefore has a single minimum. In regression problems,this can be found in one matrix operation. In classification problems,the fixed non-linearity introduced by the sigmoid output function may behandled using an iteratively re-weighted least squares function or thelike.

RBF networks may use kernel methods such as support vector machines(SVM) and Gaussian processes (where the RBF is the kernel function). Anon-linear kernel function may be used to project the input data into aspace where the learning problem can be solved using a linear model.

In embodiments, an RBF neural network may include an input layer, ahidden layer, and a summation layer. In the input layer, one neuronappears in the input layer for each predictor variable. In the case ofcategorical variables, N−1 neurons are used, where N is the number ofcategories. The input neurons may, in embodiments, standardize the valueranges by subtracting the median and dividing by the interquartilerange. The input neurons may then feed the values to each of the neuronsin the hidden layer. In the hidden layer, a variable number of neuronsmay be used (determined by the training process). Each neuron mayconsist of a radial basis function that is centered on a point with asmany dimensions as a number of predictor variables. The spread (e.g.,radius) of the RBF function may be different for each dimension. Thecenters and spreads may be determined by training. When presented with avector of input values from the input layer, a hidden neuron may computea Euclidean distance of the test case from the neuron's center point andthen apply the RBF kernel function to this distance, such as using thespread values. The resulting value may then be passed to the summationlayer. In the summation layer, the value coming out of a neuron in thehidden layer may be multiplied by a weight associated with the neuronand may add to the weighted values of other neurons. This sum becomesthe output. For classification problems, one output is produced (with aseparate set of weights and summation units) for each target category.The value output for a category is the probability that the case beingevaluated has that category. In training of an RBF, various parametersmay be determined, such as the number of neurons in a hidden layer, thecoordinates of the center of each hidden-layer function, the spread ofeach function in each dimension, and the weights applied to outputs asthey pass to the summation layer. Training may be used by clusteringalgorithms (such as k-means clustering), by evolutionary approaches, andthe like.

In embodiments, a recurrent neural network may have a time-varying,real-valued (more than just zero or one) activation (output). Eachconnection may have a modifiable real-valued weight. Some of the nodesare called labeled nodes, some output nodes, and others hidden nodes.For supervised learning in discrete time settings, training sequences ofreal-valued input vectors may become sequences of activations of theinput nodes, one input vector at a time. At each time step, eachnon-input unit may compute its current activation as a nonlinearfunction of the weighted sum of the activations of all units from whichit receives connections. The system can explicitly activate (independentof incoming signals) some output units at certain time steps.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingneural network, such as a Kohonen self-organizing neural network, suchas for visualization of views of data, such as low-dimensional views ofhigh-dimensional data. The self-organizing neural network may applycompetitive learning to a set of input data, such as from one or moresensors or other data inputs from or associated with an industrialmachine. In embodiments, the self-organizing neural network may be usedto identify structures in data, such as unlabeled data, such as in datasensed from a range of vibration, acoustic, or other analog sensors inan industrial environment, where sources of the data are unknown (suchas where vibrations may be coming from any of a range of unknownsources). The self-organizing neural network may organize structures orpatterns in the data, such that they can be recognized, analyzed, andlabeled, such as identifying structures as corresponding to vibrationsinduced by the movement of a floor, or acoustic signals created by highfrequency rotation of a shaft of a somewhat distant machine.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a recurrent neuralnetwork, which may allow for a bi-directional flow of data, such aswhere connected units (e.g., neurons or nodes) form a directed cycle.Such a network may be used to model or exhibit dynamic temporalbehavior, such as those involved in dynamic systems including a widevariety of the industrial machines and devices described throughout thisdisclosure, such as a power generation machine operating at variablespeeds or frequencies in variable conditions with variable inputs, arobotic manufacturing system, a refining system, or the like, wheredynamic system behavior involves complex interactions that an operatormay desire to understand, predict, control and/or optimize. For example,the recurrent neural network may be used to anticipate the state (suchas a maintenance state, a fault state, an operational state, or thelike), of an industrial machine, such as one performing a dynamicprocess or action. In embodiments, the recurrent neural network may useinternal memory to process a sequence of inputs, such as from othernodes and/or from sensors and other data inputs from the industrialenvironment, of the various types described herein. In embodiments, therecurrent neural network may also be used for pattern recognition, suchas for recognizing an industrial machine based on a sound signature, aheat signature, a set of feature vectors in an image, a chemicalsignature, or the like. In a non-limiting example, a recurrent neuralnetwork may recognize a shift in an operational mode of a turbine, agenerator, a motor, a compressor, or the like (such as a gear shift) bylearning to classify the shift from a training data set consisting of astream of data from tri-axial vibration sensors and/or acoustic sensorsapplied to one or more of such machines.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a modular neuralnetwork, which may comprise a series of independent neural networks(such as ones of various types described herein) that are moderated byan intermediary. Each of the independent neural networks in the modularneural network may work with separate inputs, accomplishing subtasksthat make up the task the modular network as whole is intended toperform. For example, a modular neural network may comprise a recurrentneural network for pattern recognition, such as to recognize what typeof industrial machine is being sensed by one or more sensors that areprovided as input channels to the modular network and an RBF neuralnetwork for optimizing the behavior of the machine once understood. Theintermediary may accept inputs of each of the individual neuralnetworks, process them, and create output for the modular neuralnetwork, such an appropriate control parameter, a prediction of state,or the like.

Combinations among any of the pairs, triplets, or larger combinations,of the various neural network types described herein, are encompassed bythe present disclosure. This may include combinations where an expertsystem uses one neural network for recognizing a pattern (e.g., apattern indicating a problem or fault condition) and a different neuralnetwork for self-organizing an activity or work flow based on therecognized pattern (such as providing an output governing autonomouscontrol of a system in response to the recognized condition or pattern).This may also include combinations where an expert system uses oneneural network for classifying an item (e.g., identifying a machine, acomponent, or an operational mode) and a different neural network forpredicting a state of the item (e.g., a fault state, an operationalstate, an anticipated state, a maintenance state, or the like). Modularneural networks may also include situations where an expert system usesone neural network for determining a state or context (such as a stateof a machine, a process, a work flow, a marketplace, a storage system, anetwork, a data collector, or the like) and a different neural networkfor self-organizing a process involving the state or context (e.g., adata storage process, a network coding process, a network selectionprocess, a data marketplace process, a power generation process, amanufacturing process, a refining process, a digging process, a boringprocess, or other process described herein).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a physical neuralnetwork where one or more hardware elements is used to perform orsimulate neural behavior. In embodiments, one or more hardware neuronsmay be configured to stream voltage values that represent analogvibration sensor data voltage values, to calculate velocity informationfrom analog sensor inputs representing acoustic, vibration or otherdata, to calculation acceleration information from sensor inputsrepresenting acoustic, vibration, or other data, or the like. One ormore hardware nodes may be configured to stream output data resultingfrom the activity of the neural net. Hardware nodes, which may compriseone or more chips, microprocessors, integrated circuits, programmablelogic controllers, application-specific integrated circuits,field-programmable gate arrays, or the like, may be provided to optimizethe speed, input/output efficiency, energy efficiency, signal to noiseratio, or other parameter of some part of a neural net of any of thetypes described herein. Hardware nodes may include hardware foracceleration of calculations (such as dedicated processors forperforming basic or more sophisticated calculations on input data toprovide outputs, dedicated processors for filtering or compressing data,dedicated processors for decompressing data, dedicated processors forcompression of specific file or data types (e.g., for handling imagedata, video streams, acoustic signals, vibration data, thermal images,heat maps, or the like), and the like. A physical neural network may beembodied in a data collector, such as a mobile data collector describedherein, including one that may be reconfigured by switching or routinginputs in varying configurations, such as to provide different neuralnet configurations within the data collector for handling differenttypes of inputs (with the switching and configuration optionally undercontrol of an expert system, which may include a software-based neuralnet located on the data collector or remotely). A physical, or at leastpartially physical, neural network may include physical hardware nodeslocated in a storage system, such as for storing data within anindustrial machine or in an industrial environment, such as foraccelerating input/output functions to one or more storage elements thatsupply data to or take data from the neural net. A physical, or at leastpartially physical, neural network may include physical hardware nodeslocated in a network, such as for transmitting data within, to or froman industrial environment, such as for accelerating input/outputfunctions to one or more network nodes in the net, accelerating relayfunctions, or the like. In embodiments of a physical neural network, anelectrically adjustable resistance material may be used for emulatingthe function of a neural synapse. In embodiments, the physical hardwareemulates the neurons, and software emulates the neural network betweenthe neurons. In embodiments, neural networks complement conventionalalgorithmic computers. They are versatile and can be trained to performappropriate functions without the need for any instructions, such asclassification functions, optimization functions, pattern recognitionfunctions, control functions, selection functions, evolution functions,and others.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a multilayeredfeed forward neural network, such as for complex pattern classificationof one or more items, phenomena, modes, states, or the like. Inembodiments, a multilayered feedforward neural network may be trained byan optimization technical, such as a genetic algorithm, such as toexplore a large and complex space of options to find an optimum, ornear-optimum, global solution. For example, one or more geneticalgorithms may be used to train a multilayered feedforward neuralnetwork to classify complex phenomena, such as to recognize complexoperational modes of industrial machines, such as modes involvingcomplex interactions among machines (including interference effects,resonance effects, and the like), modes involving non-linear phenomena,such as impacts of variable speed shafts, which may make analysis ofvibration and other signals difficult, modes involving critical faults,such as where multiple, simultaneous faults occur, making root causeanalysis difficult, and others. In embodiments, a multilayered feedforward neural network may be used to classify results from ultrasonicmonitoring or acoustic monitoring of an industrial machine, such asmonitoring an interior set of components within a housing, such as motorcomponents, pumps, valves, fluid handling components, and many others,such as in refrigeration systems, refining systems, reactor systems,catalytic systems, and others.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a feedforward,back-propagation multi-layer perceptron (MLP) neural network, such asfor handling one or more remote sensing applications, such as for takinginputs from sensors distributed throughout various industrialenvironments. In embodiments, the MLP neural network may be used forclassification of physical environments, such as mining environments,exploration environments, drilling environments, and the like, includingclassification of geological structures (including underground featuresand above ground features), classification of materials (includingfluids, minerals, metals, and the like), and other problems. This mayinclude fuzzy classification.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use astructure-adaptive neural network, where the structure of a neuralnetwork is adapted, such as based on a rule, a sensed condition, acontextual parameter, or the like. For example, if a neural network doesnot converge on a solution, such as classifying an item or arriving at aprediction, when acting on a set of inputs after some amount oftraining, the neural network may be modified, such as from a feedforwardneural network to a recurrent neural network, such as by switching datapaths between some subset of nodes from unidirectional to bi-directionaldata paths. The structure adaptation may occur under control of anexpert system, such as to trigger adaptation upon occurrence of atrigger, rule or event, such as recognizing occurrence of a threshold(such as an absence of a convergence to a solution within a given amountof time) or recognizing a phenomenon as requiring different oradditional structure (such as recognizing that a system is varyingdynamically or in a non-linear fashion). In one non-limiting example, anexpert system may switch from a simple neural network structure like afeedforward neural network to a more complex neural network structurelike a recurrent neural network, a convolutional neural network, or thelike upon receiving an indication that a continuously variabletransmission is being used to drive a generator, turbine, or the like ina system being analyzed.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an autoencoder,autoassociator or Diabolo neural network, which may be similar to amultilayer perceptron (“MLP”) neural network, such as where there may bean input layer, an output layer and one or more hidden layers connectingthem. However, the output layer in the auto-encoder may have the samenumber of units as the input layer, where the purpose of the MLP neuralnetwork is to reconstruct its own inputs (rather than just emitting atarget value). Therefore, the auto encoders may operate as anunsupervised learning model. An auto encoder may be used, for example,for unsupervised learning of efficient codings, such as fordimensionality reduction, for learning generative models of data, andthe like. In embodiments, an auto-encoding neural network may be used toself-learn an efficient network coding for transmission of analog sensordata from an industrial machine over one or more networks. Inembodiments, an auto-encoding neural network may be used to self-learnan efficient storage approach for storage of streams of analog sensordata from an industrial environment.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a probabilisticneural network (“PNN”), which in embodiments may comprise a multi-layer(e.g., four-layer) feedforward neural network, where layers may includeinput layers, hidden layers, pattern/summation layers and an outputlayer. In an embodiment of a PNN algorithm, a parent probabilitydistribution function (PDF) of each class may be approximated, such asby a Parzen window and/or a non-parametric function. Then, using the PDFof each class, the class probability of a new input is estimated, andBayes' rule may be employed, such as to allocate it to the class withthe highest posterior probability. A PNN may embody a Bayesian networkand may use a statistical algorithm or analytic technique, such asKernel Fisher discriminant analysis technique. The PNN may be used forclassification and pattern recognition in any of a wide range ofembodiments disclosed herein. In one non-limiting example, aprobabilistic neural network may be used to predict a fault condition ofan engine based on collection of data inputs from sensors andinstruments for the engine.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a time delayneural network (TDNN), which may comprise a feedforward architecture forsequential data that recognizes features independent of sequenceposition. In embodiments, to account for time shifts in data, delays areadded to one or more inputs, or between one or more nodes, so thatmultiple data points (from distinct points in time) are analyzedtogether. A time delay neural network may form part of a larger patternrecognition system, such as using a perceptron network. In embodiments,a TDNN may be trained with supervised learning, such as where connectionweights are trained with back propagation or under feedback. Inembodiments, a TDNN may be used to process sensor data from distinctstreams, such as a stream of velocity data, a stream of accelerationdata, a stream of temperature data, a stream of pressure data, and thelike, where time delays are used to align the data streams in time, suchas to help understand patterns that involve understanding of the variousstreams (e.g., where increases in pressure and acceleration occur as anindustrial machine overheats).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a convolutionalneural network (referred to in some cases as a CNN, a ConvNet, a shiftinvariant neural network, or a space invariant neural network), whereinthe units are connected in a pattern similar to the visual cortex of thehuman brain. Neurons may respond to stimuli in a restricted region ofspace, referred to as a receptive field. Receptive fields may partiallyoverlap, such that they collectively cover the entire (e.g., visual)field. Node responses can be calculated mathematically, such as by aconvolution operation, such as using multilayer perceptron that useminimal preprocessing. A convolutional neural network may be used forrecognition within images and video streams, such as for recognizing atype of machine in a large environment using a camera system disposed ona mobile data collector, such as on a drone or mobile robot. Inembodiments, a convolutional neural network may be used to provide arecommendation based on data inputs, including sensor inputs and othercontextual information, such as recommending a route for a mobile datacollector. In embodiments, a convolutional neural network may be usedfor processing inputs, such as for natural language processing ofinstructions provided by one or more parties involved in a workflow inan environment. In embodiments, a convolutional neural network may bedeployed with a large number of neurons (e.g., 100,000, 500,000 ormore), with multiple (e.g., 4, 5, 6 or more) layers, and with many(e.g., millions) parameters. A convolutional neural net may use one ormore convolutional nets.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a regulatoryfeedback network, such as for recognizing emergent phenomena (such asnew types of faults not previously understood in an industrialenvironment).

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a self-organizingmap (“SOM”), involving unsupervised learning. A set of neurons may learnto map points in an input space to coordinates in an output space. Theinput space can have different dimensions and topology from the outputspace, and the SOM may preserve these while mapping phenomena intogroups.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a learning vectorquantization neural net (“LVQ”). Prototypical representatives of theclasses may parameterize, together with an appropriate distance measure,in a distance-based classification scheme.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an echo statenetwork (“ESN”), which may comprise a recurrent neural network with asparsely connected, random hidden layer. The weights of output neuronsmay be changed (e.g., the weights may be trained based on feedback). Inembodiments, an ESN may be used to handle time series patterns, such as,in an example, recognizing a pattern of events associated with a gearshift in an industrial turbine, generator, or the like.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a bi-directional,recurrent neural network (“BRNN”), such as using a finite sequence ofvalues (e.g., voltage values from a sensor) to predict or label eachelement of the sequence based on both the past and the future context ofthe element. This may be done by adding the outputs of two RNNs, such asone processing the sequence from left to right, the other one from rightto left. The combined outputs are the predictions of target signals,such as those provided by a teacher or supervisor. A bi-directional RNNmay be combined with a long short-term memory RNN.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchical RNNthat connects elements in various ways to decompose hierarchicalbehavior, such as into useful subprograms. In embodiments, ahierarchical RNN may be used to manage one or more hierarchicaltemplates for data collection in an industrial environment.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a stochasticneural network, which may introduce random variations into the network.Such random variations can be viewed as a form of statistical sampling,such as Monte Carlo sampling.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a genetic scalerecurrent neural network. In such embodiments, a RNN (often a LS™) isused where a series is decomposed into a number of scales where everyscale informs the primary length between two consecutive points. A firstorder scale consists of a normal RNN, a second order consists of allpoints separated by two indices and so on. The Nth order RNN connectsthe first and last node. The outputs from all the various scales may betreated as a committee of members, and the associated scores may be usedgenetically for the next iteration.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a committee ofmachines (“CoM”), comprising a collection of different neural networksthat together “vote” on a given example. Because neural networks maysuffer from local minima, starting with the same architecture andtraining, but using randomly different initial weights often givesdifferent results. A CoM tends to stabilize the result.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an associativeneural network (“ASNN”), such as involving an extension of committee ofmachines that combines multiple feed forward neural networks and ak-nearest neighbor technique. It may use the correlation betweenensemble responses as a measure of distance amid the analyzed cases forthe kNN. This corrects the bias of the neural network ensemble. Anassociative neural network may have a memory that can coincide with atraining set. If new data become available, the network instantlyimproves its predictive ability and provides data approximation(self-learns) without retraining. Another important feature of ASNN isthe possibility to interpret neural network results by analysis ofcorrelations between data cases in the space of models.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use an instantaneouslytrained neural network (“ITNN”), where the weights of the hidden and theoutput layers are mapped directly from training vector data.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a spiking neuralnetwork, which may explicitly consider the timing of inputs. The networkinput and output may be represented as a series of spikes (such as adelta function or more complex shapes). SNNs can process information inthe time domain (e.g., signals that vary over time, such as signalsinvolving dynamic behavior of industrial machines). They are oftenimplemented as recurrent networks.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a dynamic neuralnetwork that addresses nonlinear multivariate behavior and includeslearning of time-dependent behavior, such as transient phenomena anddelay effects. Transients may include behavior of shifting industrialcomponents, such as variable speeds of rotating shafts or other rotatingcomponents.

In embodiments, cascade correlation may be used as an architecture andsupervised learning algorithm, supplementing adjustment of the weightsin a network of fixed topology. Cascade-correlation may begin with aminimal network, then automatically trains and adds new hidden units oneby one, creating a multi-layer structure. Once a new hidden unit hasbeen added to the network, its input-side weights may be frozen. Thisunit then becomes a permanent feature-detector in the network, availablefor producing outputs or for creating other, more complex featuredetectors. The cascade-correlation architecture may learn quickly,determine its own size and topology, and retain the structures it hasbuilt even if the training set changes and requires no back-propagation.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a neuro-fuzzynetwork, such as involving a fuzzy inference system in the body of anartificial neural network. Depending on the type, several layers maysimulate the processes involved in a fuzzy inference, such asfuzzification, inference, aggregation and defuzzification. Embedding afuzzy system in a general structure of a neural net as the benefit ofusing available training methods to find the parameters of a fuzzysystem.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a compositionalpattern-producing network (“CPPN”), such as a variation of anassociative neural network (“ANN”) that differs the set of activationfunctions and how they are applied. While typical ANNs often containonly sigmoid functions (and sometimes Gaussian functions), CPPNs caninclude both types of functions and many others. Furthermore, CPPNs maybe applied across the entire space of possible inputs, so that they canrepresent a complete image. Since they are compositions of functions,CPPNs in effect encode images at infinite resolution and can be sampledfor a particular display at whatever resolution is optimal.

This type of network can add new patterns without re-training. Inembodiments, methods and systems described herein that involve an expertsystem or self-organization capability may use a one-shot associativememory network, such as by creating a specific memory structure, whichassigns each new pattern to an orthogonal plane using adjacentlyconnected hierarchical arrays.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a hierarchicaltemporal memory (“HTM”) neural network, such as involving the structuraland algorithmic properties of the neocortex. HTM may use a biomimeticmodel based on memory-prediction theory. HTM may be used to discover andinfer the high-level causes of observed input patterns and sequences.

In embodiments, methods and systems described herein that involve anexpert system or self-organization capability may use a holographicassociative memory (“HAM”) neural network, which may comprise an analog,correlation-based, associative, stimulus-response system. Informationmay be mapped onto the phase orientation of complex numbers. The memoryis effective for associative memory tasks, generalization and patternrecognition with changeable attention.

In embodiments, various embodiments involving network coding may be usedto code transmission data among network nodes in neural net, such aswhere nodes are located in one or more data collectors or machines in anindustrial environment.

Clause 1. In embodiments, an expert system for processing a plurality ofinputs collected from sensors in an industrial environment, comprising:A modular neural network, where the expert system uses one type ofneural network for recognizing a pattern and a different neural networkfor self-organizing an activity in the industrial environment. 2. Asystem of clause 1, wherein the pattern indicates a fault condition of amachine. 3. A system of clause 1, wherein the self-organized activitygoverns autonomous control of a system in the environment. 4. A systemof clause 3, wherein the expert system organizes the activity based atleast in part on the recognized pattern. 5. An expert system forprocessing a plurality of inputs collected from sensors in an industrialenvironment, comprising:

a modular neural network, where the expert system uses one neuralnetwork for classifying an item and a different neural network forpredicting a state of the item. 6. A system of clause 5, whereinclassifying an item includes at least one of identifying a machine, acomponent, and an operational mode of a machine in the environment. 7. Asystem of clause 5, wherein predicting a state includes predicting atleast one of a fault state, an operational state, an anticipated state,and a maintenance state. 8. An expert system for processing a pluralityof inputs collected from sensors in an industrial environment,comprising: a modular neural network, where the expert system uses oneneural network for determining at least one of a state and a context anda different neural network for self-organizing a process involving theat least one state or context. 9. A system of clause 8, wherein the stator context includes at least one state of a machine, a process, a workflow, a marketplace, a storage system, a network, and a data collector.10. A system of clause 8, wherein the self-organized process includes atleast one of a data storage process, a network coding process, a networkselection process, a data marketplace process, a power generationprocess, a manufacturing process, a refining process, a digging process,and a boring process. 11. An expert system for processing a plurality ofinputs collected from sensors in an industrial environment, comprising:a modular neural network, comprising at least two neural networksselected from the group consisting of feed forward neural networks,radial basis function neural networks, self-organizing neural networks,Kohonen self-organizing neural networks, recurrent neural networks,modular neural networks, artificial neural networks, physical neuralnetworks, multi-layered neural networks, convolutional neural networks,a hybrids of a neural networks with another expert system, auto-encoderneural networks, probabilistic neural networks, time delay neuralnetworks, convolutional neural networks, regulatory feedback neuralnetworks, radial basis function neural networks, recurrent neuralnetworks, Hopfield neural networks, Boltzmann machine neural networks,self-organizing map (“SOM”) neural networks, learning vectorquantization (“LVQ”) neural networks, fully recurrent neural networks,simple recurrent neural networks, echo state neural networks, longshort-term memory neural networks, bi-directional neural networks,hierarchical neural networks, stochastic neural networks, genetic scaleRNN neural networks, committee of machines neural networks, associativeneural networks, physical neural networks, instantaneously trainedneural networks, spiking neural networks, neocognition neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, compositional pattern-producing neural networks, memory neuralnetworks, hierarchical temporal memory neural networks, deep feedforward neural networks, gated recurrent unit (“GCU”) neural networks,auto encoder neural networks, variational auto encoder neural networks,de-noising auto encoder neural networks, sparse auto-encoder neuralnetworks, Markov chain neural networks, restricted Boltzmann machineneural networks, deep belief neural networks, deep convolutional neuralnetworks, deconvolutional neural networks, deep convolutional inversegraphics neural networks, generative adversarial neural networks, liquidstate machine neural networks, extreme learning machine neural networks,echo state neural networks, deep residual neural networks, supportvector machine neural networks, neural Turing machine neural networks,and holographic associative memory neural networks. 12. A system forcollecting data in an industrial environment, comprising A physicalneural network embodied in a mobile data collector, wherein the mobiledata collector is adapted to be reconfigured by routing inputs invarying configurations, such that different neural net configurationsare enabled within the data collector for handling different types ofinputs. 13. A system of clause 12, wherein reconfiguration occurs undercontrol of an expert system. 14. A system of clause 13, wherein theexpert system includes a software-based neural net. 15. A system ofclause 14, wherein the software-based system is located on the datacollector. 16. A system of clause 14, wherein the software-based systemis located remotely from the data collector. 17. A system for processingdata collected from an industrial environment, the system comprising: aplurality of neural networks deployed in a cloud platform that receivesdata streams and other inputs collected from one or more industrialenvironments and transmitted to the cloud platform over one or morenetworks, wherein the neural networks are of different types. 18. Asystem of clause 17, wherein the plurality of neural networks includesat least one modular neural network. 19. A system of clause 17, whereinthe plurality of neural networks includes at least onestructure-adaptive neural network. 20. A system of clause 17, whereinthe neural networks are structured to compete with each other undercontrol of an expert system, such as by processing input data sets fromthe same industrial environment to provide outputs and comparing theoutputs to at least one measure of success. 21. A system of clause 20,wherein a genetic algorithm is used to facilitate variation andselection for the competing neural networks. 22. A system of clause 20,wherein the measure of success includes at least one of the followingmeasures: a measure of predictive accuracy, a measure of classificationaccuracy, an efficiency measure, a profit measure, a maintenancemeasure, a safety measure, and a yield measure. 23. A system,comprising: a network coding system for coding transmission of dataamong network nodes in neural network, wherein the nodes comprisehardware devices located in at least one of one or more data collectors,one or more storage systems, and one or more network devices located inan industrial environment.

Within the data collection, monitoring, and control environment of theindustrial IoT are large and various sensor sets, which make efficientsetup and timely changes to sensor data collection a challenge.Continuous collection from all sensors may be impossible given the largenumber of sensors and limited resources, such as limited availability ofpower and limited data collection and management facilities, includingvarious limitations in availability and performance of sensor datacollection devices, input/output interfaces, data transfer facilities,data storage, data analysis facilities, and the like. The number ofsensors collected from at any given time must therefore be limited in anintelligent but timely manner, both at the time of setting up initialcollection and during the process of collection, including handlingrapid changes to a present collection scheme based on a change in stateof a system, operational conditions (e.g., an alert condition, change inoperational mode, etc.), or the like. Embodiments of the methods andsystems disclosed herein may therefore include rapid route creation andmodification for routing collectors, such as by taking advantage ofhierarchical templates, execution of smart route changes, monitoring andresponding to changes in operational conditions, and the like.

In embodiments, rapid route creation and modification for datacollection in an industrial environment may take advantage ofhierarchical templates. Templates may be used to take advantage of‘like’ machinery that can utilize the same hierarchical sensor routingscheme. For example, among many possible types of machines about whichdata may be collected, the members of a certain class of motor, such asa stepper motor class, may have very similar sensor routing needs, suchas for routine operations, routine maintenance, and failure modedetection, that may be described in a common hierarchy of sensorcollection routines. The user installing a new stepper motor may thenuse the ‘stepper motor hierarchical routing template’ for the new motor.After installation, the stepper motor hierarchical routing template maythen be used to change the routing schemes for changing conditions. Theuser may optionally make adjustments to the template as needed perunique motor functions, applications, environments, modes, and the like.The use of a template for deploying a routing scheme greatly reduces thetime a user requires to configure the routing scheme for a new motor, orto deploy new routing technologies on an existing system that utilizestraditional sensor collection methods. Once the hierarchical routingtemplate is in place, the sensor collection routine may be changedquickly based on the template, thus allowing for rapid routemodification under changing conditions, such as: a change in theoperating mode of the stepper motor that requires a different subset ofsensors for monitoring, a limit alert or failure indication thatrequires a more focused subset of sensors for use in diagnosing theproblem, and the like. Hierarchical routing templates thus allow forrapid deployment of sensor routing configurations, as well as allowingthe sensed industrial environment to be altered dynamically asconditions change.

A functional hierarchy of routing templates may include differenthierarchical configurations for a component, machine, system, industrialenvironment, and the like, including all sensors and a plurality ofconfigurations formed from a subset of all sensors. At a system level,an ‘all-sensor’ configuration may include: a connection map to allsensors in a system, mapping to all onboard instrumentation sensors(e.g., monitoring points reporting within a machine or set of machines),mapping to an environment's sensors (e.g., monitoring points around themachines/equipment, but not necessarily onboard), mapping to availablesensors on data collectors (e.g., data collectors that can be flexiblyprovisioned for particular data among different kinds), a unified mapcombining different individual mappings, and the like. A routingconfiguration may be provided, such as to indicate how to implement anoperational routing scheme, a scheduled maintenance routing scheme(e.g., collecting from a greater set of overall sensors than inoperational mode, but distributed across the system, or a focused sensorset for specific components, functions, and modes), one or more failuremode routing schemes for multiple focused sensor collection groupstargeting different failure mode analyses (e.g., for a motor, onefailure mode may be for bearings, another for startup speed-torque,where a different subset of sensor data is needed based on the failuremode, such as detected in anomalous readings taken during operations ormaintenance), power savings (e.g., weather conditions necessitatingreduced plant power), and the like.

As noted, hierarchical templates may also be conditional (e.g.,rule-based), such as templates with conditional routing based onparameters, such as sensed data during a first collection period, wherea subsequent routing configuration is varied. Within the hierarchy,nodes in a graph or tree may indicate forks by which conditional logicmay be used, such as to select a given subset of sensors for a givenoperational mode. Thus, the hierarchical template may be associated witha rule-based or model-based expert system, which may facilitateautomated routing based on the hierarchical template and based onobserved conditions, such as based on a type of machine and itsoperational state, environmental context, or the like. In a non-limitingexample, a hierarchical template may have an initial collectionconfiguration and a conditional hierarchy in place to switch from theinitial collection configuration to a second collection configurationbased on the sensed conditions of an initial sensor collection.Continuing this example, among various possible machines, a conveyorsystem may have a plurality of sensors for collection in an initialcollection, but once the first data is collected and analyzed, if theconveyor is determined to be in an idle state (such as due to theabsence of a signal above a minimum threshold on a motion sensor), thenthe system may switch to a sensor data collection regime that isappropriate for the idles state of the conveyor (e.g., using a verysmall subset of the plurality of sensors, such as just using the motionsensor to detect departure from the idle state, at which point theoriginal regime may be renewed and the rest of a sensor set may bere-engaged). Thus, when the collection of sensor data detects a changedcondition to a state, an operational mode, an environmental condition,or the like, the sensor data collection may be switched to anappropriate configuration.

Hierarchical templates for one collector may be based on coordination ofrouting with that of other collectors. For instance, a collector mightbe set up to perform vibration analysis while another collector is setup to perform pressure or temperature on each machine in a set ofsimilar machines, rather than having each machine collect all of thedata on each machine, where otherwise setup for different sensor typesmay be required for each collector for each machine. Factors such as theduration of sampling required, the time required to set up a givensensor, the amount of power consumed, the time available for collectionas a whole, the data rate of input/output of a sensor and/or thecollector, the bandwidth of a channel (wired or wireless) available fortransmission of collected data, and the like can be considered inarranging the coordination of the routing of two or more collectors,such that various parallel and serial configurations may be undertakento achieve an overall effectiveness. This may include optimizing thecoordination using an expert system, such as a rule-based optimization,a model-based optimization, or optimization using machine learning.

A machine learning system may create a hierarchical template structurefor improved routing, such as for teaching the system the defaultoperating conditions (e.g., normal operations mode, systems online andaverage production), peak operations mode (max capability), slackproduction, and the like. The machine learning system may create a newhierarchical template based on monitored conditions, such as a templatebased on a production level profile, a rate of production profile, adetected failure mode pattern analysis, and the like. The application ofa new machine learning created template may be based on a mode matchingbetween current production conditions and a machine learning templatecondition (e.g., the machine learning system creates a new template fora new production profile, and applies that new template whenever thatnew profile is detected).

Rapid route creation may be enabled using one or more hierarchicalrouting templates, such as when a routing template pre-establishes arouting scheme for different conditions, and when a trigger eventexecutes a change in the sensor routing scheme to accommodate thecondition. In embodiments, the trigger event may be an automatic changein routing based on a trigger that indicates a possible failure modethat forces a change in routing scheme from operational to failure modeanalysis; a human-executed change in routing scheme based on receivedsensor data; a learned routing change based on machine learning of whento trigger a change (e.g., as based on a machine being fed with a set ofhuman-executed or human-supervised changes); a manual routing change(e.g., optional to automatic/rapid automatic change); a human executedchange based on observed device performance; and the like. Routingchanges may include: changing from an operational mode to an acceleratedmaintenance, a failure mode analysis, a power saving mode ahigh-performance/high-output mode (e.g., for peak power in a generationplant), and the like.

Switching hierarchical template configurations may be executed based onconnectivity with end-device sensors. In a highly automated collectionrouting environment (e.g., an indoor networked assembly plant) differentrouting collection configurations may be employed for fixed and flexibleindustrial layouts. In a fixed industrial layout, such as a layout witha high degree of wired connectivity between end-device sensors,automated collectors, and networks, there may be different routingconfigurations for a network routing hierarchy portion, a collectorsensor-collection hierarchy portion, a storage portion, and the like.For a more flexible industrial layout with various wired and wirelessconnections between end-device sensors, automated collectors, andnetworks, there may be different schemes. For instance, a moderatelyautomated collection routing environment may include: automaticcollection and periodic network connection; a robot-carried collectorfor periodic collection (e.g., a ground-based robot, a drone, anunderwater device, a robot with network connection, a robot withintermittent network connection, a robot that periodically uploadscollection); a routing scheme with periodic collection and automatedrouting; a scheme only collecting periodically but routed directly uponcollection; a routing scheme with periodic collection and periodicautomated routing to collect periodically; and, over longer periods oftime, periodically routing multiple collections; and the like. For alower degree of automated collection routing, there may be a combinationof: automatic collection and human-aided collectors (e.g., humanscollecting alone, humans aided by robots), scheduled collection andhuman-aided collectors (e.g., humans initiating collection, humans aidedby robots for collection initiation, human launching a drone to collectdata at a remote site), and the like.

In embodiments, and referring to FIG. 107, hierarchical templates may beutilized by a local data collection system 10500 for collection andmonitoring of data collected through a plurality of input channels10500, such as data from sensors 10514, IoT devices 10516, and the like.The local collection system 10512, also referred to herein as a datacollector 10512, may comprise a data storage 10502; a data acquisitioncircuit 10504; a data analysis circuit 10506; and the like, wherein themonitoring facilities may be deployed: locally on the data collector10512; in part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from the datacollector; and the like. A monitoring system may comprise a plurality ofinput channels communicatively coupled to the data collector 10512. Thedata storage 10502 may be structured to store a plurality of collectorroute templates 10510 and sensor specifications for sensors 10514 thatcorrespond to the input channels 10500, wherein the plurality ofcollector route templates 10510 each comprise a different sensorcollection routine. A data acquisition circuit 10504 may be structuredto interpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels10500, and a data analysis circuit 10506 structured to receive outputdata from the plurality of input channels 10500 and evaluate a currentrouting template collection routine based on the received output data,wherein the data collector 10500 is configured to switch from thecurrent routing template collection routine to an alternative routingtemplate collection routine based on the content of the output data. Themonitoring system may further utilize a machine learning system (e.g., aneural network expert system), rule-based templates (e.g., based on anoperational state of a machine with respect to which the input channelsprovide information, the input channels provide information, the inputchannels provide information), smart route changes, alarm states,network connectivity, self-organization amongst a plurality of datacollectors, coordination of sensor groups, and the like.

In embodiments, evaluation of the current routing templates may be basedon operational mode routing collection schemes, such as a normaloperational mode, a peak operational mode, an idle operational mode, amaintenance operational mode, a power saving operational mode, and thelike. As a result of monitoring, the data collector may switch from acurrent routing template collection routine because the data analysiscircuit determines a change in operating modes, such as the operatingmode changing from an operational mode to an accelerated maintenancemode, the operating mode changing from an operational mode to a failuremode analysis mode, the operating mode changing from an operational modeto a power-saving mode, the operating mode changing from an operationalmode to a high-performance mode, and the like. The data collector mayswitch from a current routing template collection routine based on asensed change in a mode of operation, such as a failure condition, aperformance condition, a power condition, a temperature condition, avibration condition, and the like. The evaluation of the current routingtemplate collection routine may be based on a collection routine withrespect to a collection parameter, such as network availability, sensoravailability, a time-based collection routine (e.g., on a schedule, overtime), and the like.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data storage structured to store aplurality of collector route templates and sensor specifications forsensors that correspond to the input channels, wherein the plurality ofcollector route templates each comprise a different sensor collectionroutine; a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of the input channels; and a data analysiscircuit structured to receive output data from the plurality of inputchannels and evaluate a current routing template collection routinebased on the received output data, wherein the data collector isconfigured to switch from the current routing template collectionroutine to an alternative routing template collection routine based onthe content of the output data. In embodiments, the system is deployedlocally on the data collector, in part locally on the data collector andin part on a remote information technology infrastructure componentapart from the collector, and the like. Each of the input channels maycorrespond to a sensor located in the environment. The evaluation of thecurrent routing template may be based on operational mode routingcollection schemes. The operational mode is at least one of a normaloperational mode, a peak operational mode, an idle operational mode, amaintenance operational mode, and a power saving operational mode. Thedata collector may switch from the current routing template collectionroutine because the data analysis circuit determines a change inoperating modes, such as where the operating mode changes from anoperational mode to an accelerated maintenance mode, from an operationalmode to a failure mode analysis mode, from an operational mode to apower saving mode, from an operational mode to high-performance mode,and the like. The data collector may switch from the current routingtemplate collection routine based on a sensed change in a mode ofoperation, such as where the sensed change is a failure condition, aperformance condition, a power condition, a temperature condition, avibration condition, and the like. The evaluation of the current routingtemplate collection routine may be based on a collection routine withrespect to a collection parameter, such as where the parameter isnetwork availability, sensor availability, a time-based collectionroutine (e.g., where a routine collects sensor data on a schedule,evaluates sensor data over time).

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data storage structured tostore a plurality of collector route templates and sensor specificationsfor sensors that correspond to the input channels, wherein the pluralityof collector route templates each comprise a different sensor collectionroutine; providing a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of the input channels; and providing adata analysis circuit structured to receive output data from theplurality of input channels and evaluate a current routing templatecollection routine based on the received output data, wherein the datacollector is configured to switch from the current routing templatecollection routine to an alternative routing template collection routinebased on the content of the output data. In embodiments, thecomputer-implemented method is deployed locally on the data collector,such as deployed in part locally on the data collector and in part on aremote information technology infrastructure component apart from thecollector, where each of the input channels correspond to a sensorlocated in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data storage structured to store a plurality of collectorroute templates and sensor specifications for sensors that correspond tothe input channels, wherein the plurality of collector route templateseach comprise a different sensor collection routine; providing a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of the input channels; and providing a data analysis circuitstructured to receive output data from the plurality of input channelsand evaluate a current routing template collection routine based on thereceived output data, wherein the data collector is configured to switchfrom the current routing template collection routine to an alternativerouting template collection routine based on the content of the outputdata. In embodiments, the instructions may be deployed locally on thedata collector, such as deployed in part locally on the data collectorand in part on a remote information technology infrastructure componentapart from the collector, where each of the input channels correspond toa sensor located in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise a data collector communicatively coupled to aplurality of input channels; a data storage structured to store aplurality of collector route templates, sensor specifications forsensors that correspond to the input channels, wherein the plurality ofcollector route templates each comprise a different sensor collectionroutine; a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of the input channels; and a machinelearning data analysis circuit structured to receive output data fromthe plurality of input channels and evaluate a current routing templatecollection routine based on the received output data received over time,wherein the machine learning data analysis circuit learns receivedoutput data patterns, wherein the data collector is configured to switchfrom the current routing template collection routine to an alternativerouting template collection routine based on the learned received outputdata patterns. In embodiments, the monitoring system may be deployedlocally on the data collector, such as deployed in part locally on thedata collector and in part on a remote information technologyinfrastructure component apart from the collector, where each of theinput channels correspond to a sensor located in the environment. Themachine learning data analysis circuit may include a neural networkexpert system. The evaluation of the current routing template may bebased on operational mode routing collection schemes. The operationalmode may be at least one of a normal operational mode, a peakoperational mode, an idle operational mode, a maintenance operationalmode, and a power saving operational mode. The data collector may switchfrom the current routing template collection routine because the dataanalysis circuit determines a change in operating modes, such as wherethe operating mode changes from an operational mode to an acceleratedmaintenance mode, from an operational mode to a failure mode analysismode, from an operational mode to a power saving mode, from anoperational mode to high-performance mode, and the like. The datacollector may switch from the current routing template collectionroutine based on a sensed change in a mode of operation, such as wherethe sensed change is a failure condition, a performance condition, apower condition, a temperature condition, a vibration condition, and thelike. The evaluation of the current routing template collection routinemay be based on a collection routine with respect to a collectionparameter, such as where the parameter is network availability, a sensoravailability, a time-based collection routine (collects sensor data on aschedule, evaluates sensor data over time).

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data storage structured tostore a plurality of collector route templates, sensor specificationsfor sensors that correspond to the input channels, wherein the pluralityof collector route templates each comprise a different sensor collectionroutine; providing a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of the input channels; and providing amachine learning data analysis circuit structured to receive output datafrom the plurality of input channels and evaluate a current routingtemplate collection routine based on the received output data receivedover time, wherein the machine learning data analysis circuit learnsreceived output data patterns, wherein the data collector is configuredto switch from the current routing template collection routine to analternative routing template collection routine based on the learnedreceived output data patterns. In embodiments, the method may bedeployed locally on the data collector, such as deployed in part locallyon the data collector and in part on a remote information technologyinfrastructure component apart from the collector, where each of theinput channels correspond to a sensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data storage structured to store a plurality of collectorroute templates, sensor specifications for sensors that correspond tothe input channels, wherein the plurality of collector route templateseach comprise a different sensor collection routine; providing a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of the input channels; and providing a machine learning dataanalysis circuit structured to receive output data from the plurality ofinput channels and evaluate a current routing template collectionroutine based on the received output data received over time, whereinthe machine learning data analysis circuit learns received output datapatterns, wherein the data collector is configured to switch from thecurrent routing template collection routine to an alternative routingtemplate collection routine based on the learned received output datapatterns. In embodiments, the instructions may be deployed locally onthe data collector, such as deployed in part locally on the datacollector and in part on a remote information technology infrastructurecomponent apart from the collector, where each of the input channelscorrespond to a sensor located in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data storage structured to store acollector route template, sensor specifications for sensors thatcorrespond to the input channels, wherein the collector route templatecomprises a sensor collection routine; a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of the inputchannels; and a data analysis circuit structured to receive output datafrom the plurality of input channels and evaluate the received outputdata with respect to a rule, wherein the data collector is configured tomodify the sensor collection routine based on the application of therule to the received output data. In embodiments, the system may bedeployed locally on the data collector, such as deployed in part locallyon the data collector and in part on a remote information technologyinfrastructure component apart from the collector, where each of theinput channels correspond to a sensor located in the environment. Therule may be based on an operational state of a machine with respect towhich the input channels provide information, on an anticipated state ofa machine with respect to which the input channels provide information,on a detected fault condition of a machine with respect to which theinput channels provide information, and the like. The evaluation of thereceived output data may be based on operational mode routing collectionschemes, where the operational mode is at least one of a normaloperational mode, a peak operational mode, an idle operational mode, amaintenance operational mode, and a power saving operational mode. Thedata collector may modify the sensor collection routine because the dataanalysis circuit determines a change in operating modes, such as wherethe operating mode changes from an operational mode to an acceleratedmaintenance mode, from an operational mode to a failure mode analysismode, from an operational mode to a power saving mode, from anoperational mode to high-performance mode, and the like. The datacollector may modify the sensor collection routine based on a sensedchange in a mode of operation, such as where the sensed change is afailure condition, a performance condition, a power condition, atemperature condition, a vibration condition, and the like. Theevaluation of the received output data may be based on a collectionroutine with respect to a collection parameter, wherein the parameter isa network availability, a sensor availability, a time-based collectionroutine (e.g., collects sensor data on a schedule or over time), and thelike.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data storage structured tostore a collector route template, sensor specifications for sensors thatcorrespond to the input channels, wherein the collector route templatecomprises a sensor collection routine; providing a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of theinput channels; and providing a data analysis circuit structured toreceive output data from the plurality of input channels and evaluatethe received output data with respect to a rule, wherein the datacollector is configured to modify the sensor collection routine based onthe application of the rule to the received output data. In embodiments,the method may be deployed locally on the data collector, such asdeployed in part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from thecollector, where each of the input channels correspond to a sensorlocated in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data storage structured to store a collector route template,sensor specifications for sensors that correspond to the input channels,wherein the collector route template comprises a sensor collectionroutine; providing a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of the input channels; and providing adata analysis circuit structured to receive output data from theplurality of input channels and evaluate the received output data withrespect to a rule, wherein the data collector is configured to modifythe sensor collection routine based on the application of the rule tothe received output data. In embodiments, the instructions may bedeployed locally on the data collector, such as deployed in part locallyon the data collector and in part on a remote information technologyinfrastructure component apart from the collector, where each of theinput channels correspond to a sensor located in the environment.

Rapid route creation and modification in an industrial environment mayemploy smart route changes based on incoming data or alarms, such aschanges enabling dynamic selection of data collection for analysis orcorrelation. Smart route changes may enable the system to alter currentrouting of sensor data based on incoming data or alarms. For instance, auser may set up a routing configuration that establishes a schedule ofsensor collection for analysis, but when the analysis (or an alarm)indicates a special need, the system may change the sensor routing toaddress that need. For example, in the case where a change in a motorvibration profile (as one example among any of the machines describedthroughout this disclosure), such as rapidly increasing the peakamplitude of shaking on at least one axis of a vibration sensor set,that indicates a potential early failure of the motor, the system maychange the routing to collect more focused data collection for analysis,such as initiating collection on more axes of the motor, initiatingcollection on additional bearings of the motor, and/or initiatingcollection using other sensors (such as temperature or heat fluxsensors), that may confirm an initial hypothesis that the failure modeis occurring or otherwise assist in analysis of the state or operationalcondition of the machine.

Detected operational mode changes may trigger a rapid route change. Forinstance, an operational mode may be detected as the result of asingle-point sensor out-of-range detection, an analysis determination,and the like, and generate a routing change. An analysis determinationmay be detected from a sensor end-point, such as through a single-pointsensor analysis, a multiple-point sensor analysis, an analysis domainanalysis (e.g., through a time profile, frequency profile, correlatedmulti-point determination), and the like. In another instance, amaintenance mode may be detected during routine maintenance, where arouting change increases data collection to capture data at a higherrate under an anomalous condition. A failure mode may be detected, suchas through an alarm that indicates near-term potential for a failure ofa machine that triggers increased data capture rate for analysis.Performance-based modes may be detected, such as detecting a level ofoutput rate (e.g., peak, slack, idle), which may then initiate changesin routing to accommodate the analysis needs for the differentperformance monitoring and metrics associated with the state. Forexample, if a high peak speed is detected for a motor, a conveyor, anassembly line, a generator, a turbine, or the like, relative tohistorical measurements over some time period, additional sensors may beengaged to watch for failures that are typically associated with peakspeeds, such as overheating (as measured by engaging a temperature orheat flux sensor), excessive noise (as measured by an acoustic or noisesensor), excessive shaking (as measured by one or more vibrationsensors), or the like.

Alarm detections may trigger a rapid route change. Alarm sources mayinclude a front-end collector, local intelligence resource, back-enddata analysis process, ambient environment detector, network qualitydetector, power quality detector, heat, smoke, noise, flooding, and thelike. Alarm types may include a single-instance anomaly detection,multiple-instance anomaly detection, simultaneous multi-sensordetection, time-clustered sensor detection (e.g., a single sensor ormultiple sensors), frequency-profile detection (e.g., increasing rate ofanomaly detection such as an alarm increasing in its occurrence overtime, a change in a frequency component of a sensor output such as amotor's physical vibration profile changing over time), and the like.

A machine learning system may change routing based on learned alarmpattern analysis. The machine learning system may learn system alarmcondition patterns, such as alarm conditions expected under normaloperating conditions, under peak operating conditions, expected overtime based on age of components (e.g., new, during operational life,during extended life, during a warrantee period), and the like. Themachine learning system may change routing based on a change in an alarmpattern, such as a system operating normally but experiencing a peakoperating alarm pattern (e.g., a system running when it should not be),a system is new but experiencing an older profile (e.g., detection ofinfant mortality), and the like. The machine learning system may changerouting based on a current alarm profile vs. an expected change inproduction condition. For example, a plant, system, or component isexperiencing above average alarm conditions just before a ramp-up ofproduction (e.g., could be foretelling of above average failures duringincreased production), just before going slack (e.g., could be anopportunity to ramp up maintenance procedures based on increased datataking routing scheme), after an unplanned event (e.g., weather, poweroutage, restart), and the like.

A rapid route change action may include: an increased rate of sampling(e.g., to a single sensor, to multiple sensors), an increase in thenumber of sensors being sampled (e.g., simultaneous sampling of othersensors on a device, coordinated sampling of similar sensors on near-bydevices), generating a burst of sampling (e.g., sampling at a high ratefor a period of time), and the like. Actions may be executed on aschedule, coordinated with a trigger, based on an operational mode, andthe like. Triggered actions may include: anomalous data, an exceededthreshold level, an operational event trigger (e.g., at startupcondition such as for startup motor torque), and the like.

A rapid route change may switch between routing schemes, such as anoperational routing scheme (e.g., a subset of sensor collection fornormal operations), a scheduled maintenance routing scheme (e.g., anincreased and focused set of sensor collection than for normaloperations), and the like. The distribution of sensor data may bechanged, such as to distribute sensor collection across the system, suchas for a sensor collection set for specific components, functions, andmodes. A failure mode routing scheme may entail multiple focused sensorcollection groups targeting different failure mode analyses (e.g., for amotor, one failure mode may be for bearings, another for startupspeed-torque) where a different subset of sensor data may be neededbased on the failure mode (e.g., as detected in anomalous readings takenduring operations or maintenance). Power saving mode routing may beexecuted when weather conditions necessitate reduced plant power.

Dynamic adjustment of route changes may be executed based onconnectivity factors, such as the factors associated with the collectoror network availability and bandwidth. For example, routing may bechanged for a device associated with an alarm detection, where changingrouting for targeted devices on the network frees up bandwidth. Changesto routing may have a duration, such as only for a pre-determined periodof time and then switching back, maintaining a change untiluser-directed, changing duration based on network availability, and thelike.

In embodiments, and referring to FIG. 109, smart route changes may beimplemented by a local data collection system 10520 for collection andmonitoring of data collected through a plurality of input channels10500, such as data from sensors 10522, IoT devices 10524, and the like.The local collection system 102, also referred to herein as a datacollector 10520, may comprise a data storage 10502, a data acquisitioncircuit 10504, a data analysis circuit 10506, a response circuit 10508,and the like, wherein the monitoring facilities may be deployed locallyon the data collector 10520, in part locally on the data collector andin part on a remote information technology infrastructure componentapart from the data collector, and the like. Smart route changes may beimplemented between data collectors, such as where a state message istransmitted between the data collectors (e.g., from an input channelthat is mounted in proximity to a second input channel, from a relatedgroup of input sensors, and the like). A monitoring system may comprisea plurality of input channels 10500 communicatively coupled to the datacollector 10520. The data acquisition circuit 10504 may be structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels10500, wherein the data acquisition circuit 10504 acquires sensor datafrom a first route of input channels for the plurality of inputchannels. The data storage 10502 may be structured to store sensor data,sensor specifications, and the like, for sensors 10524 that correspondto the input channels 10500. The data analysis circuit 10506 may bestructured to evaluate the sensor data with respect to storedanticipated state information, wherein the anticipated state informationmay include an alarm threshold level, and wherein the data analysiscircuit 10506 sets an alarm state when the alarm threshold level isexceeded for a first input channel in the first group of input channels.Further, the data analysis circuit 10506 may transmit the alarm stateacross a network to a routing control facility 10512. The responsecircuit 10508 may be structured to change the routing of the inputchannels for data collection from the first routing of input channels toan alternate routing of input channels upon reception of a routingchange indication from the routing control facility. In the case of anetwork transmission, the alternate routing of input channels mayinclude the first input channel and a group of input channels related tothe first input channel, where the data collector executes the change inrouting of the input channels if a communication parameter of thenetwork between the data collector and the routing control facility isnot met (e.g., a time-period parameter, a network connection and/orbandwidth availability parameter).

In embodiments, an alarm state may indicate a detection mode, such as anoperational mode detection comprising an out-of-range detection, amaintenance mode detection comprising an alarm detected duringmaintenance, a failure mode detection (e.g., where the controllercommunicates a failure mode detection facility), a power mode detectionwherein the alarm state is indicative of a power related limitation dataof the anticipated state information, a performance mode detectionwherein the alarm state is indicative of a high-performance limitationdata of the anticipated state information, and the like. The monitoringsystem may further include the analysis circuit setting the alarm statewhen the alarm threshold level is exceeded for an alternate inputchannel in the first group of input channels, such as where the settingof the alarm state for the first input channel and the alternate inputchannel are determined to be a multiple-instance anomaly detection,wherein the second routing of input channels comprises the first inputchannel and a second input channel, wherein the sensor data from thefirst input channel and the second input channel contribute tosimultaneous data analysis. The second routing of input channels mayinclude a change in a routing collection parameter, such as where therouting collection parameter is an increase in sampling rate, anincrease in the number of channels being sampled, a burst sampling of atleast one of the plurality of input channels, and the like.

In embodiments, and referring to FIG. 108, collector route templates10510 may be utilized for smart route changes and may be implemented bya local data collection system 10512 for collection and monitoring ofdata collected through a plurality of input channels 10500, such as datafrom sensors 10514, IoT devices 10516, and the like. The localcollection system 10512, also referred to herein as a data collector10512, may comprise a data storage 10502, a data acquisition circuit10504, a data analysis circuit 10506, a response circuit 10508, and thelike, wherein the monitoring facilities may be deployed locally on thedata collector 10512, in part locally on the data collector and in parton a remote information technology infrastructure component apart fromthe data collector, and the like.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels,wherein the data acquisition circuit acquires sensor data from a firstroute of input channels for the plurality of input channels; a datastorage structured to store sensor specifications for sensors thatcorrespond to the input channels; a data analysis circuit structured toevaluate the sensor data with respect to stored anticipated stateinformation, wherein the anticipated state information comprises analarm threshold level, and wherein the data analysis circuit sets analarm state when the alarm threshold level is exceeded for a first inputchannel in the first group of input channels; and a response circuitstructured to change the routing of the input channels for datacollection from the first routing of input channels to an alternaterouting of input channels, wherein the alternate routing of inputchannels comprise the first input channel and a group of input channelsrelated to the first input channel. In embodiments, the system may bedeployed locally on the data collector, deployed in part locally on thedata collector and in part on a remote information technologyinfrastructure component apart from the collector, wherein each of theinput channels correspond to a sensor located in the environment. Thegroup of input channels may be related to the first input channel are atleast in part taken from the plurality of input channels not included inthe first routing of input channels. An alarm state may indicate adetection mode, such as where the detection mode is an operational modedetection comprising an out-of-range detection, the detection mode is amaintenance mode detection comprising an alarm detected duringmaintenance, the detection mode is a failure mode detection. Thecontroller may communicate the failure mode detection facility, such aswhere the detection mode is a power mode detection and the alarm stateis indicative of a power related limitation data of the anticipatedstate information, the detection mode is a performance mode detectionand the alarm state is indicative of a high-performance limitation dataof the anticipated state information, and the like. The analysis circuitmay set the alarm state when the alarm threshold level is exceeded foran alternate input channel in the first group of input channels, such aswhere the setting of the alarm state for the first input channel and thealternate input channel are determined to be a multiple-instance anomalydetection, wherein the alternate routing of input channels comprises thefirst input channel and a second input channel, wherein the sensor datafrom the first input channel and the second input channel contribute tosimultaneous data analysis. The alternate routing of input channels mayinclude a change in a routing collection parameter, such as for anincrease in sampling rate, an increase in the number of channels beingsampled, a burst sampling of at least one of the plurality of inputchannels, and the like.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of the inputchannels, wherein the data acquisition circuit acquires sensor data froma first route of input channels for the plurality of input channels;providing a data storage structured to store sensor specifications forsensors that correspond to the input channels; providing a data analysiscircuit structured to evaluate the sensor data with respect to storedanticipated state information, wherein the anticipated state informationcomprises an alarm threshold level, and wherein the data analysiscircuit sets an alarm state when the alarm threshold level is exceededfor a first input channel in the first group of input channels; andproviding a response circuit structured to change the routing of theinput channels for data collection from the first routing of inputchannels to an alternate routing of input channels, wherein thealternate routing of input channels comprise the first input channel anda group of input channels related to the first input channel. Inembodiments, the system may be deployed locally on the data collector,deployed in part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from thecollector, wherein each of the input channels correspond to a sensorlocated in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions may comprise: providinga data collector communicatively coupled to a plurality of inputchannels; providing a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of the input channels, wherein the dataacquisition circuit acquires sensor data from a first route of inputchannels for the plurality of input channels; providing a data storagestructured to store sensor specifications for sensors that correspond tothe input channels; providing a data analysis circuit structured toevaluate the sensor data with respect to stored anticipated stateinformation, wherein the anticipated state information comprises analarm threshold level, and wherein the data analysis circuit sets analarm state when the alarm threshold level is exceeded for a first inputchannel in the first group of input channels; and providing a responsecircuit structured to change the routing of the input channels for datacollection from the first routing of input channels to an alternaterouting of input channels, wherein the alternate routing of inputchannels comprise the first input channel and a group of input channelsrelated to the first input channel. In embodiments, the instructions maybe deployed locally on the data collector, deployed in part locally onthe data collector and in part on a remote information technologyinfrastructure component apart from the collector, wherein each of theinput channels correspond to a sensor located in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels,wherein the data acquisition circuit acquires sensor data from a firstroute of input channels for the plurality of input channels; a datastorage structured to store sensor specifications for sensors thatcorrespond to the input channels; a data analysis circuit structured toevaluate the sensor data with respect to stored anticipated stateinformation, wherein the anticipated state information comprises analarm threshold level, and wherein the data analysis circuit sets analarm state when the alarm threshold level is exceeded for a first inputchannel in the first group of input channels and transmits the alarmstate across a network to a routing control facility; and a responsecircuit structured to change the routing of the input channels for datacollection from the first routing of input channels to an alternaterouting of input channels upon reception of a routing change indicationfrom the routing control facility, wherein the alternate routing ofinput channels comprise the first input channel and a group of inputchannels related to the first input channel, wherein the data collectorautomatically executes the change in routing of the input channels if acommunication parameter of the network between the data collector andthe routing control facility is not met. In embodiments, theinstructions may be deployed locally on the data collector, deployed inpart locally on the data collector and in part on a remote informationtechnology infrastructure component apart from the collector, whereineach of the input channels correspond to a sensor located in theenvironment. The communication parameter may be a time-period parameterwithin which the routing control facility must respond. Thecommunication parameter may be a network availability parameter, such asa network connection parameter or bandwidth requirement. The group ofinput channels related to the first input channel may be at least inpart taken from the plurality of input channels not included in thefirst routing of input channels. The alarm state may indicate adetection mode, such as an operational mode detection comprising anout-of-range detection, a maintenance mode detection comprising an alarmdetected during maintenance, and the like. The detection mode may be afailure mode detection, such as when the controller communicates thefailure mode detection facility, the alarm state is indicative of apower related limitation data of the anticipated state information, thedetection mode is a performance mode detection where the alarm state isindicative of a high-performance limitation data of the anticipatedstate information, and the like. The analysis circuit may set the alarmstate when the alarm threshold level is exceeded for an alternate inputchannel in the first group of input channels, such as where the settingof the alarm state for the first input channel and the alternate inputchannel are determined to be a multiple-instance anomaly detection,wherein the alternate routing of input channels comprises the firstinput channel and a second input channel, wherein the sensor data fromthe first input channel and the second input channel contribute tosimultaneous data analysis. The alternate routing of input channels maybe a change in a routing collection parameter, such as an increase insampling rate, is an increase in the number of channels being sampled, aburst sampling of at least one of the plurality of input channels, andthe like.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of the inputchannels, wherein the data acquisition circuit acquires sensor data froma first route of input channels for the plurality of input channels;providing a data storage structured to store sensor specifications forsensors that correspond to the input channels; providing a data analysiscircuit structured to evaluate the sensor data with respect to storedanticipated state information, wherein the anticipated state informationcomprises an alarm threshold level, and wherein the data analysiscircuit sets an alarm state when the alarm threshold level is exceededfor a first input channel in the first group of input channels andtransmits the alarm state across a network to a routing controlfacility; and providing a response circuit structured to change therouting of the input channels for data collection from the first routingof input channels to an alternate routing of input channels uponreception of a routing change indication from the routing controlfacility, wherein the alternate routing of input channels comprise thefirst input channel and a group of input channels related to the firstinput channel, wherein the data collector automatically executes thechange in routing of the input channels if a communication parameter ofthe network between the data collector and the routing control facilityis not met. In embodiments, the instructions may be deployed locally onthe data collector, deployed in part locally on the data collector andin part on a remote information technology infrastructure componentapart from the collector, wherein each of the input channels correspondto a sensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of the input channels, wherein the dataacquisition circuit acquires sensor data from a first route of inputchannels for the plurality of input channels; providing a data storagestructured to store sensor specifications for sensors that correspond tothe input channels; providing a data analysis circuit structured toevaluate the sensor data with respect to stored anticipated stateinformation, wherein the anticipated state information comprises analarm threshold level, and wherein the data analysis circuit sets analarm state when the alarm threshold level is exceeded for a first inputchannel in the first group of input channels and transmits the alarmstate across a network to a routing control facility; and providing aresponse circuit structured to change the routing of the input channelsfor data collection from the first routing of input channels to analternate routing of input channels upon reception of a routing changeindication from the routing control facility, wherein the alternaterouting of input channels comprise the first input channel and a groupof input channels related to the first input channel, wherein the datacollector automatically executes the change in routing of the inputchannels if a communication parameter of the network between the datacollector and the routing control facility is not met. In embodiments,the instructions may be deployed locally on the data collector, deployedin part locally on the data collector and in part on a remoteinformation technology infrastructure component apart from thecollector, wherein each of the input channels correspond to a sensorlocated in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a first and second data collectorcommunicatively coupled to a plurality of input channels; a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of the input channels, wherein the data acquisition circuitacquires sensor data from a first route of input channels for theplurality of input channels; a data storage structured to store sensorspecifications for sensors that correspond to the input channels; a dataanalysis circuit structured to evaluate the sensor data with respect tostored anticipated state information, wherein the anticipated stateinformation comprises an alarm threshold level, and wherein the dataanalysis circuit sets an alarm state when the alarm threshold level isexceeded for a first input channel in the first group of input channels;a communication circuit structured to communicate with a second datacollector, wherein the second data collector transmits a state messagerelated to a first input channel from the first route of input channels;and a response circuit structured to change the routing of the inputchannels for data collection from the first routing of input channels toan alternate routing of input channels based on the state message fromthe second data collector, wherein the alternate routing of inputchannel comprise the first input channel and a group of input channelsrelated to the first input sensor. In embodiments, the system may bedeployed locally on the data collector, deployed in part locally on thedata collector and in part on a remote information technologyinfrastructure component apart from the collector, wherein each of theinput channels correspond to a sensor located in the environment. Theset state message transmitted from the second data collector may be froma second input channel that is mounted in proximity to the first inputchannel. The set alarm transmitted from the second controller may befrom a second input sensor that is part of a related group of inputsensors comprising the first input sensor. The group of input channelsrelated to the first input channel may be at least in part taken fromthe plurality of input channels not included in the first routing ofinput channels. The alarm state may indicate a detection mode, such aswhere the detection mode is an operational mode detection comprising anout-of-range detection, a maintenance mode detection comprising an alarmdetected during maintenance, is a failure mode detection, and the like.The controller may communicate the failure mode detection facility, suchas where the detection mode is a power mode detection and the alarmstate is indicative of a power related limitation data of theanticipated state information, the detection mode is a performance modedetection where the alarm state is indicative of a high-performancelimitation data of the anticipated state information, and the like. Theanalysis circuit may set the alarm state when the alarm threshold levelis exceeded for an alternate input channel in the first group of inputchannels, such as where the setting of the alarm state for the firstinput channel and the alternate input channel are determined to be amultiple-instance anomaly detection, wherein the alternate routing ofinput channels comprises the first input channel and a second inputchannel, wherein the sensor data from the first input channel and thesecond input channel contribute to simultaneous data analysis. Thealternate routing of input channels may be a change in a routingcollection parameter, such as an increase in sampling rate, an increasein the number of channels being sampled, a burst sampling of at leastone of the plurality of input channels, and the like.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a first and second data collector communicativelycoupled to a plurality of input channels; providing a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of theinput channels, wherein the data acquisition circuit acquires sensordata from a first route of input channels for the plurality of inputchannels; providing a data storage structured to store sensorspecifications for sensors that correspond to the input channels;providing a data analysis circuit structured to evaluate the sensor datawith respect to stored anticipated state information, wherein theanticipated state information comprises an alarm threshold level, andwherein the data analysis circuit sets an alarm state when the alarmthreshold level is exceeded for a first input channel in the first groupof input channels; providing a communication circuit structured tocommunicate with a second data collector, wherein the second datacollector transmits a state message related to a first input channelfrom the first route of input channels, and providing a response circuitstructured to change the routing of the input channels for datacollection from the first routing of input channels to an alternaterouting of input channels based on the state message from the seconddata collector, wherein the alternate routing of input channel comprisethe first input channel and a group of input channels related to thefirst input sensor. In embodiments, the method may be deployed locallyon the data collector, deployed in part locally on the data collectorand in part on a remote information technology infrastructure componentapart from the collector, wherein each of the input channels correspondto a sensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing afirst and second data collector communicatively coupled to a pluralityof input channels; providing a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channels,wherein the data acquisition circuit acquires sensor data from a firstroute of input channels for the plurality of input channels; providing adata storage structured to store sensor specifications for sensors thatcorrespond to the input channels; providing a data analysis circuitstructured to evaluate the sensor data with respect to storedanticipated state information, wherein the anticipated state informationcomprises an alarm threshold level, and wherein the data analysiscircuit sets an alarm state when the alarm threshold level is exceededfor a first input channel in the first group of input channels;providing a communication circuit structured to communicate with asecond data collector, wherein the second data collector transmits astate message related to a first input channel from the first route ofinput channels, and providing a response circuit structured to changethe routing of the input channels for data collection from the firstrouting of input channels to an alternate routing of input channelsbased on the state message from the second data collector, wherein thealternate routing of input channel comprise the first input channel anda group of input channels related to the first input sensor. Inembodiments, the instructions may be deployed locally on the datacollector, deployed in part locally on the data collector and in part ona remote information technology infrastructure component apart from thecollector, wherein each of the input channels correspond to a sensorlocated in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input channel,wherein the data acquisition circuit acquires sensor data from a firstgroup of input channels from the plurality of input channels; a datastorage structured to store sensor specifications for sensors thatcorrespond to the input channels; a data analysis circuit structured toevaluate the sensor data with respect to stored anticipated stateinformation, wherein the anticipated state information comprises analarm threshold level, and wherein the data analysis circuit sets analarm state when the alarm threshold level is exceeded for a first inputchannel in the first group of input channel; and a response circuitstructured to change the input channels being collected from the firstgroup of input channels to an alternative group of input channels,wherein the alternate group of input channels comprise the first inputchannel and a group of input channels related to the first input sensor.In embodiments, the system may be deployed locally on the datacollector, deployed in part locally on the data collector and in part ona remote information technology infrastructure component apart from thecollector, wherein each of the input channels correspond to a sensorlocated in the environment. The group of input sensors related to thefirst input sensor may be at least in part taken from the plurality ofinput sensors not included in the first group of input sensors. Thefirst group of input channels related to the first input channel may beat least in part taken from the plurality of input channels not includedin the first routing of input channels. The alarm state may indicate adetection mode, such as where the detection mode is an operational modedetection comprising an out-of-range detection, a maintenance modedetection comprising an alarm detected during maintenance. The detectionmode may be a failure mode detection, such as where the controllercommunicates the failure mode detection facility. The detection mode maybe a power mode detection where the alarm state is indicative of a powerrelated limitation data of the anticipated state information. Thedetection mode may be a performance mode detection, where the alarmstate is indicative of a high-performance limitation data of theanticipated state information. The analysis circuit may set the alarmstate when the alarm threshold level is exceeded for an alternate inputchannel in the first group of input channels, such as when the settingof the alarm state for the first input channel and the alternate inputchannel are determined to be a multiple-instance anomaly detection,wherein the alternate routing of input channels comprises the firstinput channel and a second input channel, wherein the sensor data fromthe first input channel and the second input channel contribute tosimultaneous data analysis. An alternative group of input channels mayinclude a change in a routing collection parameter, such as where therouting collection parameter is an increase in sampling rate, anincrease in the number of channels being sampled, a burst sampling of atleast one of the plurality of input channels, and the like.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of the inputchannel, wherein the data acquisition circuit acquires sensor data froma first group of input channels from the plurality of input channels;providing a data storage structured to store sensor specifications forsensors that correspond to the input channels; providing a data analysiscircuit structured to evaluate the sensor data with respect to storedanticipated state information, wherein the anticipated state informationcomprises an alarm threshold level, and wherein the data analysiscircuit sets an alarm state when the alarm threshold level is exceededfor a first input channel in the first group of input channel; andproviding a response circuit structured to change the input channelsbeing collected from the first group of input channels to an alternativegroup of input channels, wherein the alternate group of input channelscomprise the first input channel and a group of input channels relatedto the first input sensor. In embodiments, the method may be deployedlocally on the data collector, deployed in part locally on the datacollector and in part on a remote information technology infrastructurecomponent apart from the collector, wherein each of the input channelscorrespond to a sensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of the input channel, wherein the dataacquisition circuit acquires sensor data from a first group of inputchannels from the plurality of input channels; providing a data storagestructured to store sensor specifications for sensors that correspond tothe input channels; providing a data analysis circuit structured toevaluate the sensor data with respect to stored anticipated stateinformation, wherein the anticipated state information comprises analarm threshold level, and wherein the data analysis circuit sets analarm state when the alarm threshold level is exceeded for a first inputchannel in the first group of input channel; and providing a responsecircuit structured to change the input channels being collected from thefirst group of input channels to an alternative group of input channels,wherein the alternate group of input channels comprise the first inputchannel and a group of input channels related to the first input sensor.In embodiments, the instructions may be deployed locally on the datacollector, deployed in part locally on the data collector and in part ona remote information technology infrastructure component apart from thecollector, wherein each of the input channels correspond to a sensorlocated in the environment.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collector communicatively coupled to aplurality of input channels; a data storage structured to store aplurality of collector route templates, sensor specifications forsensors that correspond to the input channels, wherein the plurality ofcollector route templates each comprise a different sensor collectionroutine; a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of the input channels, wherein the dataacquisition circuit acquires sensor data from a first route of inputchannels; and a data analysis circuit structured to evaluate the sensordata with respect to stored anticipated state information, wherein theanticipated state information comprises an alarm threshold level, andwherein the data analysis circuit sets an alarm state when the alarmthreshold level is exceeded for a first input channel in the first groupof input channels, wherein the data collector is configured to switchfrom a current routing template collection routine to an alternaterouting template collection routine based on a setting of an alarmstate. In embodiments, the system may be deployed locally on the datacollector, deployed in part locally on the data collector and in part ona remote information technology infrastructure component apart from thecollector, wherein each of the input channels correspond to a sensorlocated in the environment. The setting of the alarm state may be basedon operational mode routing collection schemes, such as where theoperational mode is at least one of a normal operational mode, a peakoperational mode, an idle operational mode, a maintenance operationalmode, and a power saving operational mode. The alarm threshold level maybe associated with a sensed change to one of the plurality of inputchannels, such as where the sensed change is a failure condition, is aperformance condition, a power condition, a temperature condition, avibration condition, and the like. The alarm state may indicate adetection mode, such as where the detection mode is an operational modedetection comprising an out-of-range detection, a maintenance modedetection comprising an alarm detected during maintenance, and the like.The detection mode may be a power mode detection, where the alarm stateis indicative of a power related limitation data of the anticipatedstate information. The detection mode may be a performance modedetection, where the alarm state is indicative of a high-performancelimitation data of the anticipated state information. The analysiscircuit may set the alarm state when the alarm threshold level isexceeded for an alternate input channel, such as wherein the setting ofthe alarm state is determined to be a multiple-instance anomalydetection. The alternate routing template may be a change to an inputchannel routing collection parameter. The routing collection parametermay be an increase in sampling rate, such as an increase in the numberof channels being sampled, a burst sampling of at least one of theplurality of input channels, and the like.

In embodiments, a computer-implemented method for implementing amonitoring system for data collection in an industrial environment maycomprise: providing a data collector communicatively coupled to aplurality of input channels; providing a data storage structured tostore a plurality of collector route templates, sensor specificationsfor sensors that correspond to the input channels, wherein the pluralityof collector route templates each comprise a different sensor collectionroutine; providing a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of the input channels, wherein the dataacquisition circuit acquires sensor data from a first route of inputchannels; and providing a data analysis circuit structured to evaluatethe sensor data with respect to stored anticipated state information,wherein the anticipated state information comprises an alarm thresholdlevel, and wherein the data analysis circuit sets an alarm state whenthe alarm threshold level is exceeded for a first input channel in thefirst group of input channels, wherein the data collector is configuredto switch from a current routing template collection routine to analternate routing template collection routine based on a setting of analarm state. In embodiments, the system may be deployed locally on thedata collector, deployed in part locally on the data collector and inpart on a remote information technology infrastructure component apartfrom the collector, wherein each of the input channels correspond to asensor located in the environment.

In embodiments, one or more non-transitory computer-readable mediacomprising computer executable instructions that, when executed, maycause at least one processor to perform actions comprising: providing adata collector communicatively coupled to a plurality of input channels;providing a data storage structured to store a plurality of collectorroute templates, sensor specifications for sensors that correspond tothe input channels, wherein the plurality of collector route templateseach comprise a different sensor collection routine; providing a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of the input channels, wherein the data acquisition circuitacquires sensor data from a first route of input channels; and providinga data analysis circuit structured to evaluate the sensor data withrespect to stored anticipated state information, wherein the anticipatedstate information comprises an alarm threshold level, and wherein thedata analysis circuit sets an alarm state when the alarm threshold levelis exceeded for a first input channel in the first group of inputchannels, wherein the data collector is configured to switch from acurrent routing template collection routine to an alternate routingtemplate collection routine based on a setting of an alarm state. Inembodiments, the instructions may be deployed locally on the datacollector, deployed in part locally on the data collector and in part ona remote information technology infrastructure component apart from thecollector, wherein each of the input channels correspond to a sensorlocated in the environment.

Methods and systems are disclosed herein for a system for datacollection in an industrial environment using intelligent management ofdata collection bands, referred to herein in some cases as smart bands.Smart bands may facilitate intelligent, situational, context-awarecollection of data, such as by a data collector (such as any of the widerange of data collector embodiments described throughout thisdisclosure). Intelligent management of data collection via smart bandsmay improve various parameters of data collection, as well as parametersof the processes, applications, and products that depend on datacollection, such as data quality parameters, consistency parameters,efficiency parameters, comprehensiveness parameters, reliabilityparameters, effectiveness parameters, storage utilization parameters,yield parameters (including financial yield, output yield, and reductionof adverse events), energy consumption parameters, bandwidth utilizationparameters, input/output speed parameters, redundancy parameters,security parameters, safety parameters, interference parameters,signal-to-noise parameters, statistical relevancy parameters, andothers. Intelligent management of smart bands may optimize across one ormore such parameters, such as based on a weighting of the value of theparameters; for example, a smart band may be managed to provide a givenlevel of redundancy for critical data, while not exceeding a specifiedlevel of energy usage. This may include using a variety of optimizationtechniques described throughout this disclosure and the documentsincorporated herein by reference.

In embodiments, such methods and systems for intelligent management ofsmart bands include an expert system and supporting technologycomponents, services, processes, modules, applications and interfaces,for managing the smart bands (collectively referred to in some cases asa smart band platform 10722), which may include a model-based expertsystem, a rule-based expert system, an expert system using artificialintelligence (such as a machine learning system, which may include aneural net expert system, a self-organizing map system, ahuman-supervised machine learning system, a state determination system,a classification system, or other artificial intelligence system), orvarious hybrids or combinations of any of the above. References to anexpert system should be understood to encompass utilization of any oneof the foregoing or suitable combinations, except where contextindicates otherwise. Intelligent management may be of data collection ofvarious types of data (e.g., vibration data, noise data and other sensordata of the types described throughout this disclosure) for eventdetection, state detection, and the like. Intelligent management mayinclude managing a plurality of smart bands each directed at supportingan identified application, process or workflow, such as confirmingprogress toward or alignment with one or more objectives, goals, rules,policies, or guidelines. Intelligent management may also involvemanaging data collection bands targeted to backing out an unknownvariable based on collection of other data (such as based on a model ofthe behavior of a system that involves the variable), selectingpreferred inputs among available inputs (including specifyingcombinations, fusions, or multiplexing of inputs), and/or specifying aninput band among available input bands.

Data collection bands, or smart bands, may include any number of itemssuch as sensors, input channels, data locations, data streams, dataprotocols, data extraction techniques, data transformation techniques,data loading techniques, data types, frequency of sampling, placement ofsensors, static data points, metadata, fusion of data, multiplexing ofdata, and the like as described herein. Smart band settings, which maybe used interchangeably with smart band and data collection band, maydescribe the configuration and makeup of the smart band, such as byspecifying the parameters that define the smart band. For example, datacollection bands, or smart bands, may include one or more frequencies tomeasure. Frequency data may further include at least one of a group ofspectral peaks, a true-peak level, a crest factor derived from a timewaveform, and an overall waveform derived from a vibration envelope, aswell as other signal characteristics described throughout thisdisclosure. Smart bands may include sensors measuring or data regardingone or more wavelengths, one or more spectra, and/or one or more typesof data from various sensors and metadata. Smart bands may include oneor more sensors or types of sensors of a wide range of types, such asdescribed throughout this disclosure and the documents incorporated byreference herein. Indeed, the sensors described herein may be used inany of the methods or systems described throughout this disclosure. Forexample, one sensor may be an accelerometer, such as one that measuresvoltage per G (“V/G”) of acceleration (e.g., 100 mV/G, 500 mV/G, 1 V/G,5 V/G, 10 V/G, and the like). In embodiments, the data collection bandcircuit may alter the makeup of the subset of the plurality of sensorsused in a smart band based on optimizing the responsiveness of thesensor, such as for example choosing an accelerometer better suited formeasuring acceleration of a low speed mixer versus one better suited formeasuring acceleration of a high speed industrial centrifuge. Choosingmay be done intelligently, such as for example with a proximity probeand multiple accelerometers disposed on a centrifuge where while at lowspeed, one accelerometer is used for measuring in the smart band andanother is used at high speeds. Accelerometers come in various types,such as piezo-electric crystal, low frequency (e.g., 10 V/G), high speedcompressors (10 MV/G), MEMS, and the like. In another example, onesensor may be a proximity probe which can be used for sleeve or tilt-padbearings (e.g., oil bath), or a velocity probe. In yet another example,one sensor may be a solid-state relay (SSR) that is structured toautomatically interface with a routed data collector (such as a mobileor portable data collector) to obtain or deliver data. In anotherexample, a mobile or portable data collector may be routed to alter themakeup of the plurality of available sensors, such as by bringing anappropriate accelerometer to a point of sensing, such as on or near acomponent of a machine. In still another example, one sensor may be atriax probe (e.g., a 100 MV/G triax probe), that in embodiments is usedfor portable data collection. In some embodiments, of a triax probe, avertical element on one axis of the probe may have a high frequencyresponse while the ones mounted horizontally may influence the frequencyresponse of the whole triax. In another example, one sensor may be atemperature sensor and may include a probe with a temperature sensorbuilt inside, such as to obtain a bearing temperature. In stilladditional examples, sensors may be ultrasonic, microphone, touch,capacitive, vibration, acoustic, pressure, strain gauges, thermographic(e.g., camera), imaging (e.g., camera, laser, IR, structured light), afield detector, an EMF meter to measure an AC electromagnetic field, agaussmeter, a motion detector, a chemical detector, a gas detector, aCBRNE detector, a vibration transducer, a magnetometer, positional,location-based, a velocity sensor, a displacement sensor, a tachometer,a flow sensor, a level sensor, a proximity sensor, a pH sensor, ahygrometer/moisture sensor, a densitometric sensor, an anemometer, aviscometer, or any analog industrial sensor and/or digital industrialsensor. In a further example, sensors may be directed at detecting ormeasuring ambient noise, such as a sound sensor or microphone, anultrasound sensor, an acoustic wave sensor, and an optical vibrationsensor (e.g., using a camera to see oscillations that produce noise). Instill another example, one sensor may be a motion detector.

Data collection bands, or smart bands, may be of or may be configured toencompass one or more frequencies, wavelengths, or spectra forparticular sensors, for particular groups of sensors, or for combinedsignals from multiple sensors (such as involving multiplexing or sensorfusion).

Data collection bands, or smart bands, may be of or may be configured toencompass one or more sensors or sensor data (including groups ofsensors and combined signals) from one or more pieces ofequipment/components, areas of an installation, disparate butinterconnected areas of an installation (e.g., a machine assembly lineand a boiler room used to power the line), or locations (e.g., abuilding in Cambridge and a building in Boston). Smart band settings,configurations, instructions, or specifications (collectively referredto herein using any one of those terms) may include where to place asensor, how frequently to sample a data point or points, the granularityat which a sample is taken (e.g., a number of sampling points perfraction of a second), which sensor of a set of redundant sensors tosample, an average sampling protocol for redundant sensors, and anyother aspect that would affect data acquisition.

Within the smart band platform 10722, an expert system, which maycomprise a neural net, a model-based system, a rule-based system, amachine learning data analysis circuit, and/or a hybrid of any of those,may begin iteration towards convergence on a smart band that isoptimized for a particular goal or outcome, such as predicting andmanaging performance, health, or other characteristics of a piece ofequipment, a component, or a system of equipment or components. Based oncontinuous or periodic analysis of sensor data, as patterns/trends areidentified, or outliers appear, or a group of sensor readings begin tochange, etc., the expert system may modify its data collection bandsintelligently. This may occur by triggering a rule that reflects a modelor understanding of system behavior (e.g., recognizing a shift inoperating mode that calls for different sensors as velocity of a shaftincreases) or it may occur under control of a neural net (either incombination with a rule-based approach or on its own), where inputs areprovided such that the neural net over time learns to select appropriatecollection modes based on feedback as to successful outcomes (e.g.,successful classification of the state of a system, successfulprediction, successful operation relative to a metric, or the like). Forexample, when a new pressure reactor is installed in a chemicalprocessing facility, data from the current data collection band may notaccurately predict the state or metric of operation of the system, thus,the machine learning data analysis circuit may begin to iterate todetermine if a new data collection band is better at predicting a state.Based on offset system data, such as from a library or other datastructure, certain sensors, frequency bands or other smart band membersmay be used in the smart band initially and data may be collected toassess performance. As the neural net iterates, other sensors/frequencybands may be accessed to determine their relative weight in identifyingperformance metrics. Over time, a new frequency band may be identified(or a new collection of sensors, a new set of configurations forsensors, or the like) as a better gauge of performance in the system andthe expert system may modify its data collection band based on thisiteration. For example, perhaps a slightly different or older associatedturbine agitator in a chemical reaction facility dampens one or morevibration frequencies while a different frequency is of higher amplitudeand present during optimal performance than what was seen in the offsetsystem. In this example, the smart band may be altered from what wassuggested by the corresponding offset system to capture the higheramplitude frequency that is present in the current system.

The expert system, in embodiments involving a neural net or othermachine learning system, may be seeded and may iterate, such as towardsconvergence on a smart band, based on feedback and operation parameters,such as described herein. Certain feedback may include utilizationmeasures, efficiency measures (e.g., power or energy utilization, use ofstorage, use of bandwidth, use of input/output use of perishablematerials, use of fuel, and/or financial efficiency), measures ofsuccess in prediction or anticipation of states (e.g., avoidance andmitigation of faults), productivity measures (e.g., workflow), yieldmeasures, and profit measures. Certain parameters may include: storageparameters (e.g., data storage, fuel storage, storage of inventory andthe like); network parameters (e.g., network bandwidth, input/outputspeeds, network utilization, network cost, network speed, networkavailability and the like); transmission parameters (e.g., quality oftransmission of data, speed of transmission of data, error rates intransmission, cost of transmission and the like); security parameters(e.g., number and/or type of exposure events; vulnerability to attack,data loss, data breach, access parameters, and the like); location andpositioning parameters (e.g., location of data collectors, location ofworkers, location of machines and equipment, location of inventoryunits, location of parts and materials, location of network accesspoints, location of ingress and egress points, location of landingpositions, location of sensor sets, location of network infrastructure,location of power sources and the like); input selection parameters,data combination parameters (e.g., for multiplexing, extraction,transformation, loading, and the like); power parameters; states (e.g.,operating modes, availability states, environmental states, fault modes,maintenance modes, anticipated states); events; and equipmentspecifications. With respect to states, operating modes may includemobility modes (direction, speed, acceleration, and the like), type ofmobility modes (e.g., rolling, flying, sliding, levitation, hovering,floating, and the like), performance modes (e.g., gears, rotationalspeeds, heat levels, assembly line speeds, voltage levels, frequencylevels, and the like), output modes, fuel conversion modes, resourceconsumption modes, and financial performance modes (e.g., yield,profitability, and the like). Availability states may refer toanticipating conditions that could cause machine to go offline orrequire backup. Environmental states may refer to ambient temperature,ambient humidity/moisture, ambient pressure, ambient wind/fluid flow,presence of pollution or contaminants, presence of interfering elements(e.g., electrical noise, vibration), power availability, and powerquality. Anticipated states may include: achieving or not achieving adesired goal, such as a specified/threshold output production rate, aspecified/threshold generation rate, an operational efficiency/failurerate, a financial efficiency/profit goal, a power efficiency/resourceutilization; an avoidance of a fault condition (e.g., overheating, slowperformance, excessive speed, excessive motion, excessivevibration/oscillation, excessive acceleration, expansion/contraction,electrical failure, running out of stored power/fuel, overpressure,excessive radiation/melt down, fire, freezing, failure of fluid flow(e.g., stuck valves, frozen fluids); mechanical failures (e.g., brokencomponent, worn component, faulty coupling, misalignment,asymmetries/deflection, damaged component (e.g., deflection, strain,stress, cracking], imbalances, collisions, jammed elements, and lost orslipping chain or belt); avoidance of a dangerous condition orcatastrophic failure; and availability (online status).

The expert system may comprise or be seeded with a model that predictsan outcome or state given a set of data (which may comprise inputs fromsensors, such as via a data collector, as well as other data, such asfrom system components, from external systems and from external datasources). For example, the model may be an operating model for anindustrial environment, machine, or workflow. In another example, themodel may be for anticipating states, for predicting fault andoptimizing maintenance, for self-organizing storage (e.g., on devices,in data pools and/or in the cloud), for optimizing data transport (suchas for optimizing network coding, network-condition-sensitive routing,and the like), for optimizing data marketplaces, and the like.

The iteration of the expert system may result in any number ofdownstream actions based on analysis of data from the smart band. In anembodiment, the expert system may determine that the system shouldeither keep or modify operational parameters, equipment or a weightingof a neural net model given a desired goal, such as aspecified/threshold output production rate, specified/thresholdgeneration rate, an operational efficiency/failure rate, a financialefficiency/profit goal, a power efficiency/resource utilization, anavoidance of a fault condition, an avoidance of a dangerous condition orcatastrophic failure, and the like. In embodiments, the adjustments maybe based on determining context of an industrial system, such asunderstanding a type of equipment, its purpose, its typical operatingmodes, the functional specifications for the equipment, the relationshipof the equipment to other features of the environment (including anyother systems that provide input to or take input from the equipment),the presence and role of operators (including humans and automatedcontrol systems), and ambient or environmental conditions. For example,in order to achieve a profit goal, a pipeline in a refinery may need tooperate for a certain amount of time a day and/or at a certain flowrate. The expert system may be seeded with a model for operation of thepipeline in a manner that results in a specified profit goal, such asindicating a given flow rate of material through the pipeline based onthe current market sale price for the material and the cost of gettingthe material into the pipeline. As it acquires data and iterates, themodel will predict whether the profit goal will be achieved given thecurrent data. Based on the results of the iteration of the expertsystem, a recommendation may be made (or a control instruction may beautomatically provided) to operate the pipeline at a higher flow rate,to keep it operational for longer or the like. Further, as the systemiterates, one or more additional sensors may be sampled in the model todetermine if their addition to the smart band would improve predicting astate. In another embodiment, the expert system may determine that thesystem should either keep or modify operational parameters, equipment ora weighting of a neural net or other model given a constraint ofoperation (e.g., meeting a required endpoint (e.g., delivery date,amount, cost, coordination with another system), operating with alimited resource (e.g., power, fuel, battery), storage (e.g., datastorage), bandwidth (e.g., local network, p2p, WAN, internet bandwidth,availability, or input/output capacity), authorization (e.g.,role-based)), a warranty limitation, a manufacturer's guideline, amaintenance guideline). For example, a constraint of operating a boilerin a refinery is that the aeration of the boiler feedwater needs to bereduced in the cycle; therefore, the boiler must coordinate with thedeaerator. In this example, the expert system is seeded with a model foroperation of the boiler in coordination with the de-aerator that resultsin a specified overall performance. As sensor data from the system isacquired, the expert system may determine that an aspect of one or bothof the boiler and aerator must be changed to continue to achieve thespecific overall performance. In a further embodiment, the expert systemmay determine that the system should either keep or modify operationalparameters, equipment or a weighting of a neural net model given anidentified choke point. In still another embodiment, the expert systemmay determine that the system should either keep or modify operationalparameters, equipment or a weighting of a neural net model given anoff-nominal operation. For example, a reciprocating compressor in arefinery that delivers gases at high pressure may be measured as havingan off-nominal operation by sensors that feed their data into an expertsystem (optionally including a neural net or other machine learningsystem). As the expert system iterates and receives the off-nominaldata, it may predict that the refinery will not achieve a specified goaland will recommend an action, such as taking the reciprocatingcompressor offline for maintenance. In another embodiment, the expertsystem may determine that the system should collect more/fewer datapoints from one or more sensors. For example, an anchor agitator in apharmaceutical processing plant may be programmed to agitate thecontents of a tank until a certain level of viscosity (e.g., as measuredin centipoise) is obtained. As the expert system collects datathroughout the run indicating an increase in viscosity, the expertsystem may recommend collecting additional data points to confirm apredicted state in the face of the increased strain on the plant systemsfrom the viscosity. In yet another embodiment, the expert system maydetermine that the system should change a data storage technique. Instill another example, the expert system may determine that the systemshould change a data presentation mode or manner. In a furtherembodiment, the expert system may determine that the system should applyone or more filters (low pass, high pass, band pass, etc.) to collecteddata. In yet a further embodiment, the expert system may determine thatthe system should collect data from a new smart band/new set of sensorsand/or begin measuring a new aspect that the neural net identifieditself. For example, various measurements may be made of paddle-typeagitator mixers operating in a pharmaceutical plant, such as mixingtimes, temperature, homogeneous substrate distribution, heat exchangewith internal structures and the tank wall or oxygen transfer rate,mechanical stress, forces and torques on agitator vessels and internalstructures, and the like. Various sensor data streams may be included ina smart band monitoring these various aspects of the paddle-typeagitator mixer, such as a flow meter, a thermometer, and others. As theexpert system iterates, perhaps having been seeded with minimal datafrom during the agitator's run, a new aspect of the operation may becomeapparent, such as the impact of pH on the state of the run. Thus, a newsmart band will be identified by the expert system that includes sensordata from a pH meter. In yet still a further embodiment, the expertsystem may determine that the system should discontinue collection ofdata from a smart band, one or more sensors, or the like. In anotherembodiment, the expert system may determine that the system shouldinitiate data collection from a new smart band, such as a new smart bandidentified by the neural net itself. In yet another embodiment, theexpert system may determine that the system should adjust theweights/biases of a model used by the expert system. In still anotherembodiment, the expert system may determine that the system shouldremove/re-task under-utilized equipment. For example, a plurality ofagitators working with a pump blasting liquid in a pharmaceuticalprocessing plant may be monitored during operation of the plant by theexpert system. Through iteration of the expert system seeded with datafrom a run of the plant with the agitators, the expert system maypredict that a state will be achieved even if one or more agitators aretaken out of service.

In embodiments, the expert system may continue iterating in adeep-learning fashion to arrive at a single smart band, after beingseeded with more than one smart band, that optimizes meeting more thanone goal. For example, there may be multiple goals tracked for a thermicheating system in a chemical processing or a food processing plant, suchas thermal efficiency and economic efficiency. Thermal efficiency forthe thermic heating system may be expressed by comparing BTUs put in tothe system, which can be obtained by knowing the amount of and qualityof the fuel being used, and the BTUs out of the system, which iscalculated using the flow out of the system and the temperaturedifferential of materials in and out of the system. Economic efficiencyof the thermic heating system may be expressed as the ratio betweencosts to run the system (including fuel, labor, materials, and services)and energy output from the system for a period of time. Data used totrack thermal efficiency may include data from a flow meter, qualitydata point(s), and a thermometer, and data used to track economicefficiency may be an energy output from the system (e.g., kWh) and costsdata. These data may be used in smart bands by the expert system topredict states, however, the expert system may iterate toward a smartband that is optimized to predict states related to both thermal andeconomic efficiency. The new smart band may include data used previouslyin the individual smart bands but may also use new data from differentsensors or data sources. In embodiments, the expert system may be seededwith a plurality of smart bands and iterate to predict various states,but may also iterate towards reducing the number of smart bands neededto predict the same set of states.

Iteration of the expert system may be governed by rules, in someembodiments. For example, the expert system may be structured to collectdata for seeding at a pre-determined frequency. The expert system may bestructured to iterate at least a number of times, such as when a newcomponent/equipment/fuel source is added, when a sensor goes off-line,or as standard practice. For example, when a sensor measuring therotation of a stirrer in a food processing line goes off-line and theexpert system begins acquiring data from a new sensor measuring the samedata points, the expert system may be structured to iterate for a numberof times before the state is utilized in or allowed to affect anydownstream actions. The expert system may be structured to trainoff-line or train in situ/online. The expert system may be structured toinclude static and/or manually input data in its smart bands. Forexample, an expert system managing smart bands associated with a mixerin a food processing plant may be structured to iterate towardspredicting a duration of mixing before the food being processed achievesa particular viscosity, wherein the smart band includes data regardingthe speed of the mixer, temperature of its contents, viscometricmeasurements and the required endpoint for viscosity and temperature ofthe food. The expert system may be structured to include aminimum/maximum number of variables.

In embodiments, the expert system may be overruled. In embodiments, theexpert system may revert to prior band settings, such as in the eventthe expert system fails, such as if a neural network fails in a neuralnet expert system, if uncertainty is too high in a model-based system,if the system is unable to resolve conflicting rules in rule-basedsystem, or the system cannot converge on a solution in any of theforegoing. For example, sensor data on an irrigation system used by theexpert system in a smart band may indicate a massive leak in the field,but visual inspection, such as by a drone, indicates no such leak. Inthis event, the expert system will revert to an original smart band forseeding the expert system. In another example, one or more point sensorson an industrial pressure cooker indicates imminent failure in a seal,but the data collection band that the expert system converged to with aweighting towards a performance metric did not identify the failure. Inthis event, the smart band will revert to an original setting or aversion of the smart band that would have also identified the imminentfailure of the pressure cooker seal. In embodiments, the expert systemmay change smart band settings in the event that a new component isadded that makes the system closer to a different offset system. Forexample, a vacuum distillation unit is added to an oil & gas refinery todistill naphthalene, but the current smart band settings for the expertsystem are derived from a refinery that distills kerosene. In thisexample, a data structure with smart band settings for various offsetsystems may be searched for a system that is more closely matched to thecurrent system. When a new offset system is identified as more closelymatched, such as one that also distill naphthalene, the new smart bandsettings (e.g., which sensors to use, where to place them, howfrequently to sample, what static data points are needed, etc. asdescribed herein) are used to seed the expert system to iterate towardspredicting a state for the system. In embodiments, the expert system maychange smart band settings in the event that a new set of offset data isavailable from a third-party library. For example, a pharmaceuticalprocessing plant may have optimized a catalytic reactor to operate in ahighly efficient way and deposited the smart band settings in a datastructure. The data structure may be continuously scanned for new smartbands that better aid in monitoring catalytic reactions and thus, resultin optimizing the operation of the reactor.

In embodiments, the expert system may be used to uncover unknownvariables. For example, the expert system may iterate to identify amissing variable to be used for further iterations, such as furtherneural net iterations. For example, an under-utilized tank in a legacycondensate/make-up water system of a power station may have an unknowncapacity because it is inaccessible and no documentation exists on thetank. Various aspects of the tank may be measured by a swarm of sensorsto arrive at an estimated volume (e.g., flow into a downstream space,duration of a dye traced solution to work through the system), then thatvolume can be fed into the neural net as a new variable in the smartband.

As described elsewhere herein, an expert system in an industrialenvironment may use sensor data to make predictions about outcomes orstates of the environment or items in the environment. Data collectionmay be of various types of data (e.g., vibration data, noise data andother sensor data of the types described throughout this disclosure) forevent detection, state detection, and the like. For example, the expertsystem may utilize ambient noise, or the overall sound environment ofthe area and/or overall vibration of the device of interest, optionallyin conjunction with other sensor data, in detecting or predicting eventsor states. For example, a reciprocating compressor in a refinery, whichmay generate its own vibration, may also have an ambient vibrationthrough contact with other aspects of the system.

In embodiments, all three types of noise (ambient noise, local noise andvibration noise) including various subsets thereof and combinations withother types of data, may be organized into large data sets, along withmeasured results, that are processed by a “deep learning” machine/expertsystem that learns to predict one or more states (e.g., maintenance,failure, or operational) or overall outcomes, such as by learning fromhuman supervision or from other feedback, such as feedback from one ormore of the systems described throughout this disclosure and thedocuments incorporated by reference herein.

Throughout this disclosure, various examples will involve machines,components, equipment, assemblies, and the like, and it should beunderstood that the disclosure could apply to any of the aforementioned.Elements of these machines operating in an industrial environment (e.g.,rotating elements, reciprocating elements, swinging elements, flexingelements, flowing elements, suspending elements, floating elements,bouncing elements, bearing elements, etc.) may generate vibrations thatmay be of a specific frequency and/or amplitude typical of the elementwhen the element is in a given operating condition or state (e.g., anormal mode of operation of a machine at a given speed, in a given gear,or the like). Changes in a parameter of the vibration may be indicativeor predictive of a state or outcome of the machine. Various sensors maybe useful in measuring vibration, such as accelerometers, velocitytransducers, imaging sensors, acoustic sensors, and displacement probes,which may collectively be known as vibration sensors. Vibration sensorsmay be mounted to the machine, such as permanently or temporarily (e.g.,adhesive, hook-and-loop, or magnetic attachment), or may be disposed ona mobile or portable data collector. Sensed conditions may be comparedto historical data to identify or predict a state, condition or outcome.Typical faults that can be identified using vibration analysis include:machine out of balance, machine out of alignment, resonance, bentshafts, gear mesh disturbances, blade pass disturbances, vane passdisturbances, recirculation & cavitation, motor faults (rotor & stator),bearing failures, mechanical looseness, critical machine speeds, and thelike, as well as excessive friction, clutch slipping, belt problems,suspension and shock absorption problems, valve and other fluid leaks,under-pressure states in lubrication and other fluid systems,overheating (such as due to many of the above), blockage or freezing ofengagement of mechanical systems, interference effects, and other faultsdescribed throughout this disclosure and in the documents incorporatedby reference.

Given that machines are frequently found adjacent to or working inconcert with other machinery, measuring the vibration of the machine maybe complicated by the presence of various noise components in theenvironment or associated vibrations that the machine may be subjectedto. Indeed, the ambient and/or local environment may have its ownvibration and/or noise pattern that may be known. In embodiments, thecombination of vibration data with ambient and/or local noise or otherambient sensed conditions may form its own pattern, as will be furtherdescribed herein.

In embodiments, measuring vibration noise may involve one or morevibration sensors on or in a machine to measure vibration noise of themachine that occurs continuously or periodically. Analysis of thevibration noise may be performed, such as filtering, signalconditioning, spectral analysis, trend analysis, and the like. Analysismay be performed on aggregate or individual sensor measurements toisolate vibration noise of equipment to obtain a characteristicvibration, vibration pattern or “vibration fingerprint” of the machine.The vibration fingerprints may be stored in a data structure, orlibrary, of vibration fingerprints. The vibration fingerprints mayinclude frequencies, spectra (i.e., frequency vs. amplitude),velocities, peak locations, wave peak shapes, waveform shapes, waveenvelope shapes, accelerations, phase information, phase shifts(including complex phase measurements) and the like. Vibrationfingerprints may be stored in the library in association with aparameter by which it may be searched or sorted. The parameters mayinclude a brand or type of machine/component/equipment, location ofsensor(s) attachment or placement, duty cycle of the equipment/machine,load sharing of the equipment/machine, dynamic interactions with otherdevices, RPM, flow rate, pressure, other vibration drivingcharacteristic, voltage of line power, age of equipment, time ofoperation, known neighboring equipment, associated auxiliaryequipment/components, size of space equipment is in, material ofplatform for equipment, heat flux, magnetic fields, electrical fields,currents, voltage, capacitance, inductance, aspect of a product, andcombinations (e.g., simple ratios) of the same. Vibration fingerprintsmay be obtained for machines under normal operation or for other periodsof operation (e.g., off-nominal operation, malfunction, maintenanceneeded, faulty component, incorrect parameters of operation, otherconditions, etc.) and can be stored in the library for comparison tocurrent data. The library of vibration fingerprints may be stored asindicators with associated predictions, states, outcomes and/or events.Trend analysis data of measured vibration fingerprints can indicate timebetween maintenance events/failure events.

In embodiments, vibration noise may be used by the expert system toconfirm the status of a machine, such as a favorable operation, aproduction rate, a generation rate, an operational efficiency, afinancial efficiency (e.g., output per cost), a power efficiency, andthe like. In embodiments, the expert system may make a comparison of thevibration noise with a stored vibration fingerprint. In otherembodiments, the expert system may be seeded with vibration noise andinitial feedback on states and outcomes in order to learn to predictother states and outcomes. For example, a center pivot irrigation systemmay be remotely monitored by attached vibration sensors to provide ameasured vibration noise that can be compared to a library of vibrationfingerprints to confirm that the system is operating normally. If thesystem is not operating normally, the expert system may automaticallydispatch a field crew or drone to investigate. In another example of avacuum distillation unit in a refinery, the vibration noise may becompared, such as by the expert system, to stored vibration fingerprintsin a library to confirm a production rate of diesel. In a furtherexample, the expert system may be seeded with vibration noise for apipeline under conditions of a normal production rate and as the expertsystem iterates with current data (e.g., altered vibration noise, andpossibly other altered parameters), it may predict that the productionrate has increased as caused by the alterations. Measurements may becontinually analyzed in this way to remotely monitor operation.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to predict when maintenance is required (e.g., off-nominalmeasurement, artifacts in signal, etc.), such as when vibration noise ismatched to a condition when the equipment/component requiredmaintenance, vibration noise exceeds a threshold/limit, vibration noiseexceeds a threshold/limit or matches a library vibration fingerprinttogether with one or more additional parameters, as described herein.For example, when the vibration fingerprint from a turbine agitator in apharmaceutical processing plant matches a vibration fingerprint for aturbine agitator when it required a replacement bearing, the expertsystem may cause an action to occur, such as immediately shutting downthe agitator or scheduling its shutdown and maintenance.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to predict a failure or an imminent failure. For example,vibration noise from a gas agitator in a pharmaceutical processing plantmay be matched to a condition when the agitator previously failed or wasabout to fail. In this example, the expert system may immediately shutdown the agitator, schedule its shutdown, or cause a backup agitator tocome online. In another example, vibration noise from a pump blastingliquid agitator in a chemical processing plant may exceed a threshold orlimit and the expert system may cause an investigation into the cause ofthe excess vibration noise, shut down the agitator, or the like. Inanother example, vibration noise from an anchor agitator in apharmaceutical processing plant may exceed a threshold/limit or match alibrary vibration fingerprint together with one or more additionalparameters (see parameters herein), such as a decreased flow rate,increased temperature, or the like. Using vibration noise taken togetherwith the parameters, the expert system may more reliably predict thefailure or imminent failure.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to predict or diagnose a problem (e.g., unbalanced, misaligned,worn, or damaged) with the equipment or an external source contributingvibration noise to the equipment. For example, when the vibration noisefrom a paddle-type agitator mixer matches a vibration fingerprint from aprior imbalance, the expert system may immediately shut down the mixer.

In embodiments, when the expert system makes a prediction of an outcomeor state using vibration noise, the expert system may perform adownstream action, or cause it to be performed. Downstream actions mayinclude: triggering an alert of a failure, imminent failure, ormaintenance event; shutting down equipment/component; initiatingmaintenance/lubrication/alignment; deploying a field technician;recommending a vibration absorption/dampening device; modifying aprocess to utilize backup equipment/component; modifying a process topreserve products/reactants, etc.; generating/modifying a maintenanceschedule; coupling the vibration fingerprint with duty cycle of theequipment, RPM, flow rate, pressure, temperature or othervibration-driving characteristic to obtain equipment/component statusand generate a report, and the like. For example, vibration noise for acatalytic reactor in a chemical processing plant may be matched to acondition when the catalytic reactor required maintenance. Based on thispredicted state of required maintenance, the expert system may deploy afield technician to perform the maintenance.

In embodiments, the library may be updated if a changed parameterresulted in a new vibration fingerprint, or if a predicted outcome orstate did not occur in the absence of mitigation. In embodiments, thelibrary may be updated if a vibration fingerprint was associated with analternative state than what was predicted by the library. The update mayoccur after just one time that the state that actually occurred did notmatch the predicted state from the library. In other embodiments, it mayoccur after a threshold number of times. In embodiments, the library maybe updated to apply one or more rules for comparison, such as rules thatgovern how many parameters to match along with the vibrationfingerprint, or the standard deviation for the match in order to acceptthe predicted outcome.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to determine if a change in a system parameter external orinternal to the machine has an effect on its intrinsic operation. Inembodiments, a change in one or more of a temperature, flow rate,materials in use, duration of use, power source, installation, or otherparameter (see parameters above) may alter the vibration fingerprint ofa machine. For example, in a pressure reactor in a chemical processingplant, the flow rate and a reactant may be changed. The changes mayalter the vibration fingerprint of the machine such that the vibrationfingerprint stored in the library for normal operation is no longercorrect.

Ambient noise, or the overall sound environment of the area and/oroverall vibration of the device of interest, optionally in conjunctionwith other ambient sensed conditions, may be used in detecting orpredicting events, outcomes, or states. Ambient noise may be measured bya microphone, ultrasound sensors, acoustic wave sensors, opticalvibration sensors (e.g., using a camera to see oscillations that producenoise), or “deep learning” neural networks involving various sensorarrays that learn, using large data sets, to identify patterns, soundstypes, noise types, etc. In an embodiment, the ambient sensed conditionmay relate to motion detection. For example, the motion may be aplatform motion (e.g., vehicle, oil platform, suspended platform onland, etc.) or an object motion (e.g., moving equipment, people, robots,parts (e.g., fan blades or turbine blades), etc.). In an embodiment, theambient sensed condition may be sensed by imaging, such as to detect alocation and nature of various machines, equipment, and other objects,such as ones that might impact local vibration. In an embodiment, theambient sensed condition may be sensed by thermal detection and imaging(e.g., for presence of people; presence of heat sources that may affectperformance parameters, etc.). In an embodiment, the ambient sensedcondition may be sensed by field detection (e.g., electrical, magnetic,etc.). In an embodiment, the ambient sensed condition may be sensed bychemical detection (e.g., smoke, other conditions). Any sensor data maybe used by the expert system to provide an ambient sensed condition foranalysis along with the vibration fingerprint to predict an outcome,event, or state. For example, an ambient sensed condition near a stirreror mixer in a food processing plant may be the operation of a spaceheater during winter months, wherein the ambient sensed condition mayinclude an ambient noise and an ambient temperature.

In an aspect, local noise may be the noise or vibration environmentwhich is ambient, but known to be locally generated. The expert systemmay filter out ambient noise, employ common mode noise removal, and/orphysically isolate the sensing environment.

In embodiments, a system for data collection in an industrialenvironment may use ambient, local and vibration noise for prediction ofoutcomes, events, and states. A library may be populated with each ofthe three noise types for various conditions (e.g., start up, shut down,normal operation, other periods of operation as described elsewhereherein). In other embodiments, the library may be populated with noisepatterns representing the aggregate ambient, local, and/or vibrationnoise. Analysis (e.g., filtering, signal conditioning, spectralanalysis, trend analysis) may be performed on the aggregate noise toobtain a characteristic noise pattern and identify changes in noisepattern as possible indicators of a changed condition. A library ofnoise patterns may be generated with established vibration fingerprintsand local and ambient noise that can be sorted by a parameter (seeparameters herein), or other parameters/features of the local andambient environment (e.g., company type, industry type, products,robotic handling unit present/not present, operating environment, flowrates, production rates, brand or type of auxiliary equipment (e.g.,filters, seals, coupled machinery)). The library of noise patterns maybe used by an expert system, such as one with machine learning capacity,to confirm a status of a machine, predict when maintenance is required(e.g., off-nominal measurement, artifacts in signal), predict a failureor an imminent failure, predict/diagnose a problem, and the like.

Based on a current noise pattern, the library may be consulted or usedto seed an expert system to predict an outcome, event, or state based onthe noise pattern. Based on the prediction, the expert system may one ormore of trigger an alert of a failure, imminent failure, or maintenanceevent, shut down equipment/component/line, initiatemaintenance/lubrication/alignment, deploy a field technician, recommenda vibration absorption/dampening device, modify a process to utilizebackup equipment/component, modify a process to preserveproducts/reactants, etc., generate/modify a maintenance schedule, or thelike.

For example, a noise pattern for a thermic heating system in apharmaceutical plant or cooking system may include local, ambient, andvibration noise. The ambient noise may be a result of, for example,various pumps to pump fuel into the system. Local noise may be a resultof a local security camera chirping with every detection of motion.Vibration noise may result from the combustion machinery used to heatthe thermal fluid. These noise sources may form a noise pattern whichmay be associated with a state of the thermic system. The noise patternand associated state may be stored in a library. An expert system usedto monitor the state of the thermic heating system may be seeded withnoise patterns and associated states from the library. As current dataare received into the expert system, it may predict a state based onhaving learned noise patterns and associated states.

In another example, a noise pattern for boiler feed water in a refinerymay include local and ambient noise. The local noise may be attributedto the operation of, for example, a feed pump feeding the feed waterinto a steam drum. The ambient noise may be attributed to nearby fans.These noise sources may form a noise pattern which may be associatedwith a state of the boiler feed water. The noise pattern and associatedstate may be stored in a library. An expert system used to monitor thestate of the boiler may be seeded with noise patterns and associatedstates from the library. As current data are received into the expertsystem, it may predict a state based on having learned noise patternsand associated states.

In yet another example, a noise pattern for a storage tank in a refinerymay include local, ambient, and vibration noise. The ambient noise maybe a result of, for example, a pump that pumps a product into the tank.Local noise may be a result of a fan ventilating the tank room.Vibration noise may result from line noise of a power supply into thestorage tank. These noise sources may form a noise pattern which may beassociated with a state of the storage tank. The noise pattern andassociated state may be stored in a library. An expert system used tomonitor the state of the storage tank may be seeded with noise patternsand associated states from the library. As current data are receivedinto the expert system, it may predict a state based on having learnednoise patterns and associated states.

In another example, a noise pattern for condensate/make-up water systemin a power station may include vibration and ambient noise. The ambientnoise may be attributed to nearby fans. The vibration noise may beattributed to the operation of the condenser. These noise sources mayform a noise pattern which may be associated with a state of thecondensate/make-up water system. The noise pattern and associated statemay be stored in a library. An expert system used to monitor the stateof the condensate/make-up water system may be seeded with noise patternsand associated states from the library. As current data are receivedinto the expert system, it may predict a state based on having learnednoise patterns and associated states.

A library of noise patterns may be updated if a changed parameterresulted in a new noise pattern or if a predicted outcome or state didnot occur in the absence of mitigation of a diagnosed problem. A libraryof noise patterns may be updated if a noise pattern resulted in analternative state than what was predicted by the library. The update mayoccur after just one time that the state that actually occurred did notmatch the predicted state from the library. In other embodiments, it mayoccur after a threshold number of times. In embodiments, the library maybe updated to apply one or more rules for comparison, such as rules thatgovern how many parameters to match along with the noise pattern, or thestandard deviation for the match in order to accept the predictedoutcome. For example, a baffle may be replaced in a static agitator in apharmaceutical processing plant which may result in a changed noisepattern. In another example, as the seal on a pressure cooker in a foodprocessing plant ages, the noise pattern associated with the pressurecooker may change.

In embodiments, the library of vibration fingerprints, noise sourcesand/or noise patterns may be available for subscription. The librariesmay be used in offset systems to improve operation of the local system.Subscribers may subscribe at any level (e.g., component, machinery,installation, etc.) in order to access data that would normally not beavailable to them, such as because it is from a competitor, or is froman installation of the machinery in a different industry not typicallyconsidered. Subscribers may search on indicators/predictors based on orfiltered by system conditions, or update an indicator/predictor withproprietary data to customize the library. The library may furtherinclude parameters and metadata auto-generated by deployed sensorsthroughout an installation, onboard diagnostic systems andinstrumentation and sensors, ambient sensors in the environment, sensors(e.g., in flexible sets) that can be put into place temporarily, such asin one or more mobile data collectors, sensors that can be put intoplace for longer term use, such as being attached to points of intereston devices or systems, and the like.

In embodiments, a third party (e.g., RMOs, manufacturers) can aggregatedata at the component level, equipment level, factory/installation leveland provide a statistically valid data set against which to optimizetheir own systems. For example, when a new installation of a machine iscontemplated, it may be beneficial to review a library for best datapoints to acquire in making state predictions. For example, a particularsensor package may be recommended to reliably determine if there will bea failure. For example, if vibration noise of equipment coupled withparticular levels of local noise or other ambient sensed conditionsreliably is an indicator of imminent failure, a given vibrationtransducer/temp/microphone package observing those elements may berecommended for the installation. Knowing such information may informthe choice to rent or buy a piece of machinery or associated warrantiesand service plans, such as based on knowing the quantity and depth ofinformation that may be needed to reliably maintain the machinery.

In embodiments, manufacturers may utilize the library to rapidly collectin-service information for machines to draft engineering specificationsfor new customers.

In embodiments, noise and vibration data may be used to remotely monitorinstalls and automatically dispatch a field crew.

In embodiments, noise and vibration data may be used to audit a system.For example, equipment running outside the range of a licensed dutycycle may be detected by a suite of vibration sensors and/orambient/local noise sensors. In embodiments, alerts may be triggered ofpotential out-of-warranty violations based on data from vibrationsensors and/or ambient/local noise sensors.

In embodiments, noise and vibration data may be used in maintenance.This may be particularly useful where multiple machines are deployedthat may vibrationally interact with the environment, such as two largegenerating machines on the same floor or platform with each other, suchas in power generation plants.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment, may include a plurality of sensors 10802selected among vibration sensors, ambient environment condition sensorsand local sensors for collecting non-vibration data proximal to amachine in the environment, the plurality of sensors 10802communicatively coupled to a data collector 10804, a data collectioncircuit 10808 structured to collect output data 10810 from the pluralityof sensors 10802, and a machine learning data analysis circuit 10812structured to receive the output data 10810 and learn received outputdata patterns 10814 predictive of at least one of an outcome and astate. The state may correspond to an outcome relating to a machine inthe environment, an anticipated outcome relating to a machine in theenvironment, an outcome relating to a process in the environment, or ananticipated outcome relating to a process in the environment. The systemmay be deployed on the data collector 10804 or distributed between thedata collector 10804 and a remote infrastructure. The data collector10804 may include the data collection circuit 10808. The ambientenvironment condition or local sensors include one or more of a noisesensor, a temperature sensor, a flow sensor, a pressure sensor, achemical sensor, a vibration sensor, an acceleration sensor, anaccelerometer, a Pressure sensor, a force sensor, a position sensor, alocation sensor, a velocity sensor, a displacement sensor, a temperaturesensor, a thermographic sensor, a heat flux sensor, a tachometer sensor,a motion sensor, a magnetic field sensor, an electrical field sensor, agalvanic sensor, a current sensor, a flow sensor, a gaseous flow sensor,a non-gaseous fluid flow sensor, a heat flow sensor, a particulate flowsensor, a level sensor, a proximity sensor, a toxic gas sensor, achemical sensor, a CBRNE sensor, a pH sensor, a hygrometer, a moisturesensor, a densitometer, an imaging sensor, a camera, an SSR, a triaxprobe, an ultrasonic sensor, a touch sensor, a microphone, a capacitivesensor, a strain gauge, an EMF meter, and the like.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment may include a data collection circuit 10808structured to collect output data 10810 from a plurality of sensors10802 selected among vibration sensors, ambient environment conditionsensors and local sensors for collecting non-vibration data proximal toa machine in the environment, the plurality of sensors 10802communicatively coupled to a data collection circuit 10808, and amachine learning data analysis circuit 10812 structured to receive theoutput data 10810 and learn received output data patterns 10814predictive of at least one of an outcome and a state, wherein themonitoring system 10800 is structured to determine if the output datamatches a learned received output data pattern. The machine learningdata analysis circuit 10812 may be structured to learn received outputdata patterns 10814 by being seeded with a model 10816. The model 10816may be a physical model, an operational model, or a system model. Themachine learning data analysis circuit 10812 may be structured to learnreceived output data patterns 10814 based on the outcome or the state.The monitoring system 10700 keeps or modifies operational parameters orequipment based on the predicted outcome or the state. The datacollection circuit 10808 collects more or fewer data points from one ormore of the plurality of sensors 10802 based on the learned receivedoutput data patterns 10814, the outcome or the state. The datacollection circuit 10808 changes a data storage technique for the outputdata based on the learned received output data patterns 10814, theoutcome, or the state. The data collector 10804 changes a datapresentation mode or manner based on the learned received output datapatterns 10814, the outcome, or the state. The data collection circuit10808 applies one or more filters (low pass, high pass, band pass, etc.)to the output data. The data collection circuit 10808 adjusts theweights/biases of the machine learning data analysis circuit 10812, suchas in response to the learned received output data patterns 10814. Themonitoring system 10800 removes/re-tasks under-utilized equipment basedon one or more of the learned received output data patterns 10814, theoutcome, or the state. The machine learning data analysis circuit 10812may include a neural network expert system. The machine learning dataanalysis circuit 10812 may be structured to learn received output datapatterns 10814 indicative of progress/alignment with one or moregoals/guidelines, wherein progress/alignment of each goal/guideline isdetermined by a different subset of the plurality of sensors 10802. Themachine learning data analysis circuit 10812 may be structured to learnreceived output data patterns 10814 indicative of an unknown variable.The machine learning data analysis circuit 10812 may be structured tolearn received output data patterns 10814 indicative of a preferredinput sensor among available input sensors. The machine learning dataanalysis circuit 10812 may be disposed in part on a machine, on one ormore data collection circuits 10808, in network infrastructure, in thecloud, or any combination thereof. The output data 10810 from thevibration sensors forms a vibration fingerprint, which may include oneor more of a frequency, a spectrum, a velocity, a peak location, a wavepeak shape, a waveform shape, a wave envelope shape, an acceleration, aphase information, and a phase shift. The data collection circuit 10808may apply a rule regarding how many parameters of the vibrationfingerprint to match or the standard deviation for the match in order toidentify a match between the output data 10810 and the learned receivedoutput data pattern. The state may be one of a normal operation, amaintenance required, a failure, or an imminent failure. The monitoringsystem 10800 may trigger an alert, shut down equipment/component/line,initiate maintenance/lubrication/alignment based on the predictedoutcome or state, deploy a field technician based on the predictedoutcome or state, recommend a vibration absorption/dampening devicebased on the predicted outcome or state, modify a process to utilizebackup equipment/component based on the predicted outcome or state, andthe like. The monitoring system 10800 may modify a process to preserveproducts/reactants, etc. based on the predicted outcome or state. Themonitoring system 10800 may generate or modify a maintenance schedulebased on the predicted outcome or state. The data collection circuit10808 may include the data collection circuit 10808. The system may bedeployed on the data collection circuit 10808 or distributed between thedata collection circuit 10808 and a remote infrastructure.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment may include a data collection circuit 10808structured to collect output data 10810 from a plurality of sensors10802 selected among vibration sensors, ambient environment conditionsensors and local sensors for collecting non-vibration data proximal toa machine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808, and amachine learning data analysis circuit 10812 structured to receive theoutput data 10810 and learn received output data patterns 10814predictive of at least one of an outcome and a state, wherein themonitoring system 10800 is structured to determine if the output datamatches a learned received output data pattern and keep or modifyoperational parameters or equipment based on the determination.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment may include a data collection circuit 10808structured to collect output data 10810 from the plurality of sensors10802 selected among vibration sensors, ambient environment conditionsensors and local sensors for collecting non-vibration data proximal toa machine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808, and amachine learning data analysis circuit 10812 structured to receive theoutput data 10810 and learn received output data patterns 10814predictive of at least one of an outcome and a state, wherein the outputdata 10810 from the vibration sensors forms a vibration fingerprint. Thevibration fingerprint may include one or more of a frequency, aspectrum, a velocity, a peak location, a wave peak shape, a waveformshape, a wave envelope shape, an acceleration, a phase information, anda phase shift. The data collection circuit 10808 may apply a ruleregarding how many parameters of the vibration fingerprint to match orthe standard deviation for the match in order to identify a matchbetween the output data 10810 and the learned received output datapattern. The monitoring system 10800 may be structured to determine ifthe output data matches a learned received output data pattern and keepor modify operational parameters or equipment based on thedetermination.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment may include a data collection band circuit 10818that identifies a subset of the plurality of sensors 10802 from which toprocess output data, the sensors selected among vibration sensors,ambient environment condition sensors and local sensors for collectingnon-vibration data proximal to a machine in the environment, theplurality of sensors 10802 communicatively coupled to a data collectionband circuit 10818, a data collection circuit 10808 structured tocollect the output data 10810 from the subset of plurality of sensors10802, and a machine learning data analysis circuit 10812 structured toreceive the output data 10810 and learn received output data patterns10814 predictive of at least one of an outcome and a state, wherein whenthe learned received output data patterns 10814 do not reliably predictthe outcome or the state, the data collection band circuit 10818 altersat least one parameter of at least one of the plurality of sensors10802. A controller 10806 identifies a new data collection band circuit10818 based on one or more of the learned received output data patterns10814 and the outcome or state. The machine learning data analysiscircuit 10812 may be further structured to learn received output datapatterns 10814 indicative of a preferred input data collection bandamong available input data collection bands. The system may be deployedon the data collection circuit 10808 or distributed between the datacollection circuit 10808 and a remote infrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may include a data collection circuit 10808 structured tocollect output data 10810 from a plurality of sensors 10802, the sensorsselected among vibration sensors, ambient environment condition sensorsand local sensors for collecting non-vibration data proximal to amachine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808, whereinthe output data 10810 from the vibration sensors is in the form of avibration fingerprint, a data structure 10820 comprising a plurality ofvibration fingerprints and associated outcomes, and a machine learningdata analysis circuit 10812 structured to receive the output data 10810and learn received output data patterns 10814 predictive of an outcomeor a state based on processing of the vibration fingerprints. Themachine learning data analysis circuit 10812 may be seeded with one ofthe plurality of vibration fingerprints from the data structure 10820.The data structure 10820 may be updated if a changed parameter resultedin a new vibration fingerprint or if a predicted outcome did not occurin the absence of mitigation. The data structure 10820 may be updatedwhen the learned received output data patterns 10814 do not reliablypredict the outcome or the state. The system may be deployed on the datacollection circuit or distributed between the data collection circuitand a remote infrastructure.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment may include a data collection circuit 10808structured to collect output data 10810 from a plurality of sensors10802 selected among vibration sensors, ambient environment conditionsensors and local sensors for collecting non-vibration data proximal toa machine in the environment, the plurality of sensors 10802communicatively coupled to a data collection circuit 10808, wherein theoutput data 10810 from the plurality of sensors 10802 is in the form ofa noise pattern, a data structure 10820 comprising a plurality of noisepatterns and associated outcomes, and a machine learning data analysiscircuit 10812 structured to receive the output data 10810 and learnreceived output data patterns 10814 predictive of an outcome or a statebased on processing of the noise patterns.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a plurality of sensors selected amongvibration sensors, ambient environment condition sensors and localsensors for collecting non-vibration data proximal to a machine in theenvironment, the plurality of sensors communicatively coupled to a datacollector; a data collection circuit structured to collect output datafrom the plurality of sensors; and a machine learning data analysiscircuit structured to receive the output data and learn received outputdata patterns predictive of at least one of an outcome and a state. Thestate may correspond to an outcome, anticipated outcome, outcomerelating to a process, as relating to a machine in the environment. Thesystem may be deployed on the data collector. The system may bedistributed between the data collector and a remote infrastructure. Theambient environment condition sensors may include a noise sensor, atemperature sensor, a flow sensor, a pressure sensor, include a chemicalsensor, a noise sensor, a temperature sensor, a flow sensor, a pressuresensor, a chemical sensor, a vibration sensor, an acceleration sensor,an accelerometer, a pressure sensor, a force sensor, a position sensor,a location sensor, a velocity sensor, a displacement sensor, atemperature sensor, a thermographic sensor, a heat flux sensor, atachometer sensor, a motion sensor, a magnetic field sensor, anelectrical field sensor, a galvanic sensor, a current sensor, a flowsensor, a gaseous flow sensor, a non-gaseous fluid flow sensor, a heatflow sensor, a particulate flow sensor, a level sensor, a proximitysensor, a toxic gas sensor, a chemical sensor, a CBRNE sensor, a pHsensor, a hygrometer, a moisture sensor, a densitometer, an imagingsensor, a camera, an SSR, a triax probe, an ultrasonic sensor, a touchsensor, a microphone, a capacitive sensor, a strain gauge, and an EMFmeter. The local sensors may comprise one or more of a vibration sensor,an acceleration sensor, an accelerometer, a pressure sensor, a forcesensor, a position sensor, a location sensor, a velocity sensor, adisplacement sensor, a temperature sensor, a thermographic sensor, aheat flux sensor, a tachometer sensor, a motion sensor, a magnetic fieldsensor, an electrical field sensor, a galvanic sensor, a current sensor,a flow sensor, a gaseous flow sensor, a non-gaseous fluid flow sensor, aheat flow sensor, a particulate flow sensor, a level sensor, a proximitysensor, a toxic gas sensor, a chemical sensor, a CBRNE sensor, a pHsensor, a hygrometer, a moisture sensor, a densitometer, an imagingsensor, a camera, an SSR, a triax probe, an ultrasonic sensor, a touchsensor, a microphone, a capacitive sensor, a strain gauge, and an EMFmeter.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection circuit structured tocollect output data from a plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternspredictive of at least one of an outcome and a state, wherein themonitoring system is structured to determine if the output data matchesa learned received output data pattern. In embodiments, the machinelearning data analysis circuit may be structured to learn receivedoutput data patterns by being seeded with a model, such as where themodel is a physical model, an operational model, or a system model. Themachine learning data analysis circuit may be structured to learnreceived output data patterns based on the outcome or the state. Themonitoring system may keep or modify operational parameters or equipmentbased on the predicted outcome or the state. The data collection circuitcollects data points from one or more of the plurality of sensors basedon the learned received output data patterns, the outcome, or the state.The data collection circuit may change a data storage technique for theoutput data based on the learned received output data patterns, theoutcome, or the state. The data collection circuit may change a datapresentation mode or manner based on the learned received output datapatterns, the outcome, or the state. The data collection circuit mayapply one or more filters (low pass, high pass, band pass, etc.) to theoutput data. The data collection circuit may adjust the weights/biasesof the machine learning data analysis circuit, such as where theadjustment is in response to the learned received output data patterns.The monitoring system may remove, or re-task under-utilized equipmentbased on one or more of the learned received output data patterns, theoutcome, or the state. The machine learning data analysis circuit mayinclude a neural network expert system. The machine learning dataanalysis circuit may be structured to learn received output datapatterns indicative of progress/alignment with one or more goals orguidelines, such as where progress or alignment of each goal orguideline is determined by a different subset of the plurality ofsensors. The machine learning data analysis circuit may be structured tolearn received output data patterns indicative of an unknown variable.The machine learning data analysis circuit may be structured to learnreceived output data patterns indicative of a preferred input sensoramong available input sensors. The machine learning data analysiscircuit may be disposed in part on a machine, on one or more datacollectors, in network infrastructure, in the cloud, or any combinationthereof. The output data from the vibration sensors may form a vibrationfingerprint, such as where the vibration fingerprint includes one ormore of a frequency, a spectrum, a velocity, a peak location, a wavepeak shape, a waveform shape, a wave envelope shape, an acceleration, aphase information, and a phase shift. The data collection circuit mayapply a rule regarding how many parameters of the vibration fingerprintto match or the standard deviation for the match in order to identify amatch between the output data and the learned received output datapattern. The state may be one of a normal operation, a maintenancerequired, a failure, or an imminent failure. The monitoring system maytrigger an alert based on the predicted outcome or state. The monitoringsystem may shut down equipment, component, or line based on thepredicted outcome or state. The monitoring system may initiatemaintenance, lubrication, or alignment based on the predicted outcome orstate. The monitoring system may deploy a field technician based on thepredicted outcome or state. The monitoring system may recommend avibration absorption or dampening device based on the predicted outcomeor state. The monitoring system may modify a process to utilize backupequipment or a component based on the predicted outcome or state. Themonitoring system may modify a process to preserve products or reactantsbased on the predicted outcome or state. The monitoring system maygenerate or modify a maintenance schedule based on the predicted outcomeor state. The system may be distributed between the data collector and aremote infrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection circuit structured tocollect output data from a plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternspredictive of at least one of an outcome and a state, wherein themonitoring system is structured to determine if the output data matchesa learned received output data pattern and keep or modify operationalparameters or equipment based on the determination.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection circuit structured tocollect output data from a plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternspredictive of at least one of an outcome and a state, wherein the outputdata from the vibration sensors forms a vibration fingerprint. Inembodiments, the vibration fingerprint may comprise one or more of afrequency, a spectrum, a velocity, a peak location, a wave peak shape, awaveform shape, a wave envelope shape, an acceleration, a phaseinformation, and a phase shift. The data collection circuit may apply arule regarding how many parameters of the vibration fingerprint to matchor the standard deviation for the match in order to identify a matchbetween the output data and the learned received output data pattern.The monitoring system may be structured to determine if the output datamatches a learned received output data pattern and keep or modifyoperational parameters or equipment based on the determination.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection band circuit that identifiesa subset of a plurality of sensors from which to process output data,the sensors selected among vibration sensors, ambient environmentcondition sensors and local sensors for collecting non-vibration dataproximal to a machine in the environment, the plurality of sensorscommunicatively coupled to the data collection band circuit; a datacollection circuit structured to collect the output data from the subsetof plurality of sensors; and a machine learning data analysis circuitstructured to receive the output data and learn received output datapatterns predictive of at least one of an outcome and a state whereinwhen the learned received output data patterns do not reliably predictthe outcome or the state, the data collection band circuit alters atleast one parameter of at least one of the plurality of sensors. Inembodiments, the controller may identify a new data collection bandcircuit based on one or more of the learned received output datapatterns and the outcome or state. The machine learning data analysiscircuit may be further structured to learn received output data patternsindicative of a preferred input data collection band among availableinput data collection bands. The system may be distributed between thedata collection circuit and a remote infrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise a data collection circuit structured to collectoutput data from the plurality of sensors, the sensors selected amongvibration sensors, ambient environment condition sensors and localsensors for collecting non-vibration data proximal to a machine in theenvironment and being communicatively coupled to the data collectioncircuit, wherein the output data from the vibration sensors is in theform of a vibration fingerprint; a data structure comprising a pluralityof vibration fingerprints and associated outcomes; and a machinelearning data analysis circuit structured to receive the output data andlearn received output data patterns predictive of an outcome or a statebased on processing of the vibration fingerprints. The machine learningdata analysis circuit may be seeded with one of the plurality ofvibration fingerprints from the data structure. The data structure maybeupdated if a changed parameter resulted in a new vibration fingerprintor if a predicted outcome did not occur in the absence of mitigation.The data structure may be updated when the learned received output datapatterns do not reliably predict the outcome or the state. The systemmay be distributed between the data collection circuit and a remoteinfrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise a data collection circuit structured to collectoutput data from the plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit, wherein the output data from the plurality of sensors is in theform of a noise pattern; a data structure comprising a plurality ofnoise patterns and associated outcomes; and a machine learning dataanalysis circuit structured to receive the output data and learnreceived output data patterns predictive of an outcome or a state basedon processing of the noise patterns.

The term industrial system (and similar terms) as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, an industrial system includes anylarge scale process system, mechanical system, chemical system, assemblyline, oil and gas system (including, without limitation, production,transportation, exploration, remote operations, offshore operations,and/or refining), mining system (including, without limitation,production, exploration, transportation, remote operations, and/orunderground operations), rail system (yards, trains, shipments, etc.),construction, power generation, aerospace, agriculture, food processing,and/or energy generation. Certain components may not be consideredindustrial individually, but may be considered industrially in anaggregated system—for example a single fan, motor, and/or engine may benot an industrial system, but may be a part of a larger system and/or beaccumulated with a number of other similar components to be consideredan industrial system and/or a part of an industrial system. In certainembodiments, a system may be considered an industrial system for somepurposes but not for other purposes—for example a large data server farmmay be considered an industrial system for certain sensing operations,such as temperature detection, vibration, or the like, but not anindustrial system for other sensing operations such as gas composition.Additionally, in certain embodiments, otherwise similar looking systemsmay be differentiated in determining whether such system are industrialsystems, and/or which type of industrial system. For example, one dataserver farm may not, at a given time, have process stream flow ratesthat are critical to operation, while another data server farm may haveprocess stream flow rates that are critical to operation (e.g., acoolant flow stream), and accordingly one data farm server may be anindustrial system for a data collection and/or sensing improvementprocess or system, while the other is not. Accordingly, the benefits ofthe present disclosure may be applied in a wide variety of systems, andany such systems may be considered an industrial system herein, while incertain embodiments a given system may not be considered an industrialsystem herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system, how to combineprocesses and systems from the present disclosure to enhance operationsof the contemplated system. Certain considerations for the person ofskill in the art, in determining whether a contemplated system is anindustrial system and/or whether aspects of the present disclosure canbenefit or enhance the contemplated system include, without limitation:the accessibility of portions of the system to positioning sensingdevices; the sensitivity of the system to capital costs (e.g., initialinstallation) and operating costs (e.g., optimization of processes,reduction of power usage); the transmission environment of the system(e.g., availability of broadband internet; satellite coverage; wirelesscellular access; the electro-magnetic (“EM”) environment of the system;the weather, temperature, and environmental conditions of the system;the availability of suitable locations to run wires, network lines, andthe like; the presence and/or availability of suitable locations fornetwork infrastructure, router positioning, and/or wireless repeaters);the availability of trained personnel to interact with computingdevices; the desired spatial, time, and/or frequency resolution ofsensed parameters in the system; the degree to which a system or processis well understood or modeled; the turndown ratio in system operations(e.g., high load differential to low load; high flow differential to lowflow; high temperature operation differential to low temperatureoperation); the turndown ratio in operating costs (e.g., effects ofpersonnel costs based on time (day, season, etc.); effects of powerconsumption cost variance with time, throughput, etc.); the sensitivityof the system to failure, down-time, or the like; the remoteness of thecontemplated system (e.g., transport costs, time delays, etc.); and/orqualitative scope of change in the system over the operating cycle(e.g., the system runs several distinct processes requiring a variablesensing environment with time; time cycle and nature of changes such asperiodic, event driven, lead times generally available, etc.). Whilespecific examples of industrial systems and considerations are describedherein for purposes of illustration, any system benefitting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term sensor (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, sensor includes any deviceconfigured to provide a sensed value representative of a physical value(e.g., temperature, force, pressure) in a system, or representative of aconceptual value in a system at least having an ancillary relationshipto a physical value (e.g., work, state of charge, frequency, phase,etc.).

Example and non-limiting sensors include vibration, acceleration, noise,pressure, force, position, location, velocity, displacement,temperature, heat flux, speed, rotational speed (e.g., a tachometer),motion, accelerometers, magnetic field, electrical field, galvanic,current, flow (gas, fluid, heat, particulates, particles, etc.), level,proximity, gas composition, fluid composition, toxicity, corrosiveness,acidity, pH, humidity, hygrometer measures, moisture, density (bulk orspecific), ultrasound, imaging, analog, and/or digital sensors. The listof sensed values is a non-limiting example, and the benefits of thepresent disclosure in many applications can be realized independent ofthe sensor type, while in other applications the benefits of the presentdisclosure may be dependent upon the sensor type.

The sensor type and mechanism for detection may be any type of sensorunderstood in the art. Without limitation, an accelerometer may be anytype and scaling, for example 500 mV per g (1 g=9.8 m/s²), 100 mV, 1 Vper g, 5 V per g, 10 V per g, 10 MV per g, as well as any frequencycapability. It will be understood for accelerometers, and for all sensortypes, that the scaling and range may be competing (e.g., in a fixed-bitor low bit A/D system), and/or selection of high resolution scaling witha large range may drive up sensor and/or computing costs, which may beacceptable in certain embodiments, and may be prohibitive in otherembodiments. Example and non-limiting accelerometers includepiezo-electric devices, high resolution and sampling speed positiondetection devices (e.g., laser based devices), and/or detection of otherparameters (strain, force, noise, etc.) that can be correlated toacceleration and/or vibration. Example and non-limiting proximity probesinclude electro-magnetic devices (e.g., Hall effect, VariableReluctance, etc.), a sleeve/oil film device, and/or determination ofother parameters than can be correlated to proximity. An examplevibration sensor includes a tri-axial probe, which may have highfrequency response (e.g., scaling of 100 MV/g). Example and non-limitingtemperature sensors include thermistors, thermocouples, and/or opticaltemperature determination.

A sensor may, additionally or alternatively, provide a processed value(e.g., a de-bounced, filtered, and/or compensated value) and/or a rawvalue, with processing downstream (e.g., in a data collector,controller, plant computer, and/or on a cloud-based data receiver). Incertain embodiments, a sensor provides a voltage, current, data file(e.g., for images), or other raw data output, and/or a sensor provides avalue representative of the intended sensed measurement (e.g., atemperature sensor may communicate a voltage or a temperature value).Additionally or alternatively, a sensor may communicate wirelessly,through a wired connection, through an optical connection, or by anyother mechanism. The described examples of sensor types and/orcommunication parameters are non-limiting examples for purposes ofillustration.

Additionally or alternatively, in certain embodiments, a sensor is adistributed physical device—for example where two separate sensingelements coordinate to provide a sensed value (e.g., a position sensingelement and a mass sensing element may coordinate to provide anacceleration value). In certain embodiments, a single physical devicemay form two or more sensors, and/or parts of more than one sensor. Forexample, a position sensing element may form a position sensor and avelocity sensor, where the same physical hardware provides the senseddata for both determinations.

The term smart sensor, smart device (and similar terms) as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure, a smart sensor includesany sensor and aspect thereof as described throughout the presentdisclosure. A smart sensor includes an increment of processing reflectedin the sensed value communicated by the sensor, including at least basicsensor processing (e.g., de-bouncing, filtering, compensation,normalization, and/or output limiting), more complex compensations(e.g., correcting a temperature value based on known effects of currentenvironmental conditions on the sensed temperature value, common mode orother noise removal, etc.), a sensing device that provides the sensedvalue as a network communication, and/or a sensing device thataggregates a number of sensed values for communication (e.g., multiplesensors on a device communicated out in a parseable or deconvolutablemanner or as separate messages; multiple sensors providing a value to asingle smart sensor, which relays sensed values on to a data collector,controller, plant computer, and/or cloud-based data receiver). The useof the term smart sensor is for purposes of illustration, and whether asensor is a smart sensor can depend upon the context and thecontemplated system, and can be a relative description compared to othersensors in the contemplated system. Thus, a given sensor havingidentical functionality may be a smart sensor for the purposes of onecontemplated system, and just a sensor for the purposes of anothercontemplated system, and/or may be a smart sensor in a contemplatedsystem during certain operating conditions, and just a sensor for thepurposes of the same contemplated system during other operatingconditions.

The terms sensor fusion, fused sensors, and similar terms, as utilizedherein, should be understood broadly, except where context indicatesotherwise, without limitation to any other aspect or description of thepresent disclosure. A sensor fusion includes a determination of secondorder data from sensor data, and further includes a determination ofsecond order data from sensor data of multiple sensors, includinginvolving multiplexing of streams of data, combinations of batches ofdata, and the like from the multiple sensors. Second order data includesa determination about a system or operating condition beyond that whichis sensed directly. For example, temperature, pressure, mixing rate, andother data may be analyzed to determine which parameters areresult-effective on a desired outcome (e.g., a reaction rate). Thesensor fusion may include sensor data from multiple sources, and/orlongitudinal data (e.g., taken over a period of time, over the course ofa process, and/or over an extent of components in a plant—for exampletracking a number of assembled parts, a virtual slug of fluid passingthrough a pipeline, or the like). The sensor fusion may be performed inreal-time (e.g., populating a number of sensor fusion determinationswith sensor data as a process progresses), off-line (e.g., performed ona controller, plant computer, and/or cloud-based computing device),and/or as a post-processing operation (e.g., utilizing historical data,data from multiple plants or processes, etc.). In certain embodiments, asensor fusion includes a machine pattern recognition operation—forexample where an outcome of a process is given to the machine and/ordetermined by the machine, and the machine pattern recognition operationdetermines result-effective parameters from the detected sensor valuespace to determine which operating conditions were likely to be thecause of the outcome and/or the off-nominal result of the outcome (e.g.,process was less effective or more effective than nominal, failed,etc.). In certain embodiments, the outcome may be a quantitative outcome(e.g., 20% more product was produced than a nominal run) or aqualitative outcome (e.g., product quality was unacceptable, component Xof the contemplated system failed during the process, component X of thecontemplated system required a maintenance or service event, etc.).

In certain embodiments, a sensor fusion operation is iterative orrecursive—for example an estimated set of result effective parameters isupdated after the sensor fusion operation, and a subsequent sensorfusion operation is performed on the same data or another data set withan updated set of the result effective parameters. In certainembodiments, subsequent sensor fusion operations include adjustments tothe sensing scheme—for example higher resolution detections (e.g., intime, space, and/or frequency domains), larger data sets (and consequentcommitment of computing and/or networking resources), changes in sensorcapability and/or settings (e.g., changing an A/D scaling, range,resolution, etc.; changing to a more capable sensor and/or more capabledata collector, etc.) are performed for subsequent sensor fusionoperations. In certain embodiments, the sensor fusion operationdemonstrates improvements to the contemplated system (e.g., productionquantity, quality, and/or purity, etc.) such that expenditure ofadditional resources to improve the sensing scheme are justified. Incertain embodiments, the sensor fusion operation provides forimprovement in the sensing scheme without incremental cost—for exampleby narrowing the number of result effective parameters and therebyfreeing up system resources to provide greater resolution, samplingrates, etc., from hardware already present in the contemplated system.In certain embodiments, iterative and/or recursive sensor fusion isperformed on the same data set, a subsequent data set, and/or ahistorical data set. For example, high resolution data may already bepresent in the system, and a first sensor fusion operation is performedwith low resolution data (e.g., sampled from the high resolution dataset), such as to allow for completion of sensor fusion processingoperations within a desired time frame, within a desired processor,memory, and/or network utilization, and/or to allow for checking a largenumber of variables as potential result effective parameters. In afurther example, a greater number of samples from the high resolutiondata set may be utilized in a subsequent sensor fusion operation inresponse to confidence that improvements are present, narrowing of thepotential result effective variables, and/or a determination that higherresolution data is required to determine the result effective parametersand/or effective values for such parameters.

The described operations and aspects for sensor fusion are non-limitingexamples, and one of skill in the art, having the benefit of thedisclosures herein and information ordinarily available about acontemplated system, can readily design a system to utilize and/orbenefit from a sensor fusion operation. Certain considerations for asystem to utilize and/or benefit from a sensor fusion operation include,without limitation: the number of components in the system; the cost ofcomponents in the system; the cost of maintenance and/or down-time forthe system; the value of improvements in the system (productionquantity, quality, yield, etc.); the presence, possibility, and/orconsequences of undesirable system outcomes (e.g., side products,thermal and/or luminary events, environmental benefits or consequences,hazards present in the system); the expense of providing a multiplicityof sensors for the system; the complexity between system inputs andsystem outputs; the availability and cost of computing resources (e.g.,processing, memory, and/or communication throughput); the size/scale ofthe contemplated system and/or the ability of such a system to generatestatistically significant data; whether offset systems exist, includingwhether data from offset systems is available and whether combining datafrom offset systems will generate a statistically improved data setrelative to the system considered alone; and/or the cost of upgrading,improving, or changing a sensing scheme for the contemplated system. Thedescribed considerations for a contemplated system that may benefit fromor utilize a sensor fusion operation are non-limiting illustrations.

Certain systems, processes, operations, and/or components are describedin the present disclosure as “offset systems” or the like. An offsetsystem is a system distinct from a contemplated system, but havingrelevance to the contemplated system. For example, a contemplatedrefinery may have an “offset refinery,” which may be a refinery operatedby a competitor, by a same entity operating the contemplated refinery,and/or a historically operated refinery that no longer exists. Theoffset refinery bears some relevant relationship to the contemplatedrefinery, such as utilizing similar reactions, process flows, productionvolumes, feed stock, effluent materials, or the like. A system which isan offset system for one purpose may not be an offset system for anotherpurpose. For example, a manufacturing process utilizing conveyor beltsand similar motors may be an offset process for a contemplatedmanufacturing process for the purpose of tracking product movement,understanding motor operations and failure modes, or the like, but maynot be an offset process for product quality if the products beingproduced have distinct quality outcome parameters. Any industrial systemcontemplated herein may have an offset system for certain purposes. Oneof skill in the art, having the benefit of the present disclosure andinformation ordinarily available for a contemplated system, can readilydetermine what is disclosed by an offset system or offset aspect of asystem.

Any one or more of the terms computer, computing device, processor,circuit, and/or server include a computer of any type, capable to accessinstructions stored in communication thereto such as upon anon-transient computer readable medium, whereupon the computer performsoperations of systems or methods described herein upon executing theinstructions. In certain embodiments, such instructions themselvescomprise a computer, computing device, processor, circuit, and/orserver. Additionally or alternatively, a computer, computing device,processor, circuit, and/or server may be a separate hardware device, oneor more computing resources distributed across hardware devices, and/ormay include such aspects as logical circuits, embedded circuits,sensors, actuators, input and/or output devices, network and/orcommunication resources, memory resources of any type, processingresources of any type, and/or hardware devices configured to beresponsive to determined conditions to functionally execute one or moreoperations of systems and methods herein.

Certain operations described herein include interpreting, receiving,and/or determining one or more values, parameters, inputs, data, orother information. Operations including interpreting, receiving, and/ordetermining any value parameter, input, data, and/or other informationinclude, without limitation: receiving data via a user input; receivingdata over a network of any type; reading a data value from a memorylocation in communication with the receiving device; utilizing a defaultvalue as a received data value; estimating, calculating, or deriving adata value based on other information available to the receiving device;and/or updating any of these in response to a later received data value.In certain embodiments, a data value may be received by a firstoperation, and later updated by a second operation, as part of thereceiving a data value. For example, when communications are down,intermittent, or interrupted, a first operation to interpret, receive,and/or determine a data value may be performed, and when communicationsare restored an updated operation to interpret, receive, and/ordetermine the data value may be performed.

Certain logical groupings of operations herein, for example methods orprocedures of the current disclosure, are provided to illustrate aspectsof the present disclosure. Operations described herein are schematicallydescribed and/or depicted, and operations may be combined, divided,re-ordered, added, or removed in a manner consistent with the disclosureherein. It is understood that the context of an operational descriptionmay require an ordering for one or more operations, and/or an order forone or more operations may be explicitly disclosed, but the order ofoperations should be understood broadly, where any equivalent groupingof operations to provide an equivalent outcome of operations isspecifically contemplated herein. For example, if a value is used in oneoperational step, the determining of the value may be required beforethat operational step in certain contexts (e.g., where the time delay ofdata for an operation to achieve a certain effect is important), but maynot be required before that operation step in other contexts (e.g.,where usage of the value from a previous execution cycle of theoperations would be sufficient for those purposes). Accordingly, incertain embodiments an order of operations and grouping of operations asdescribed is explicitly contemplated herein, and in certain embodimentsre-ordering, subdivision, and/or different grouping of operations isexplicitly contemplated herein.

Referencing FIG. 112, an example system 10902 for data collection in anindustrial environment includes an industrial system 10904 having anumber of components 10906, and a number of sensors 10908, wherein eachof the sensors 10908 is operatively coupled to at least one of thecomponents 10906. The selection, distribution, type, and communicativesetup of sensors depends upon the application of the system 10902 and/orthe context.

The example system 10902 further includes a sensor communication circuit10920 (reference FIG. 113) that interprets a number of sensor datavalues 10948 in response to a sensed parameter group 10928. The sensedparameter group 10928 includes a description of which sensors 10908 aresampled at which times, including at least the selected samplingfrequency, a process stage wherein a particular sensor may be providinga value of interest, and the like. An example system includes the sensedparameter group 10928 being a fused number of sensors 10926, for examplea set of sensors believed to encompass detection of operating conditionsof the system that affect a desired output, such as production output,quality, efficiency, profitability, purity, maintenance or servicepredictions of components in the system, failure mode predictions, andthe like. In a further embodiment, the recognized pattern value 10930further includes a secondary value 10932 including a value determined inresponse to the fused number of sensors 10926.

In certain embodiments, sensor data values 10948 are provided to a datacollector 10910, which may be in communication with multiple sensors10908 and/or with a controller 10914. In certain embodiments, a plantcomputer 10912 is additionally or alternatively present. In the examplesystem, the controller 10914 is structured to functionally executeoperations of the sensor communication circuit 10920, patternrecognition circuit 10922, and/or the sensor learning circuit 10924, andis depicted as a separate device for clarity of description. Aspects ofthe controller 10914 may be present on the sensors 10908, the datacontroller 10910, the plant computer 10912, and/or on a cloud computingdevice 10916. In certain embodiments, all aspects of the controller10914 may be present in another device depicted on the system 10902. Theplant computer 10912 represents local computing resources, for exampleprocessing, memory, and/or network resources, that may be present and/orin communication with the industrial system 10904. In certainembodiments, the cloud computing device 10916 represents computingresources externally available to the industrial system 10904, forexample over a private network, intra-net, through cellularcommunications, satellite communications, and/or over the internet. Incertain embodiments, the data controller 10910 may be a computingdevice, a smart sensor, a MUX box, or other data collection devicecapable to receive data from multiple sensors and to pass-through thedata and/or store data for later transmission. An example datacontroller 10910 has no storage and/or limited storage, and selectivelypasses sensor data therethrough, with a subset of the sensor data beingcommunicated at a given time due to bandwidth considerations of the datacontroller 10910, a related network, and/or imposed by environmentalconstraints. In certain embodiments, one or more sensors and/orcomputing devices in the system 10902 are portable devices—for example aplant operator walking through the industrial system may have a smartphone, which the system 10902 may selectively utilize as a datacontroller 10910, sensor 10908—for example to enhance communicationthroughput, sensor resolution, and/or as a primary method forcommunicating sensor data values 10948 to the controller 10914.

The example system 10902 further includes a pattern recognition circuit10922 that determines a recognized pattern value 10930 in response to aleast a portion of the sensor data values 10948.

The example system 10902 further includes a sensor learning circuit10924 that updates the sensed parameter group 10928 in response to therecognized pattern value 10930. The example sensor communication circuit10920 further adjusts the interpreting the sensor data values 10948 inresponse to the updated sensed parameter group 10928.

An example system 10902 further includes the pattern recognition circuit10922 and the sensor learning circuit 10924 iteratively performing thedetermining the recognized pattern value 10930 and the updating thesensed parameter group 10928 to improve a sensing performance value10934. For example, the pattern recognition circuit 10922 may addsensors, remove sensors, and/or change sensor setting to modify thesensed parameter group 10928 based upon sensors which appear to beeffective or ineffective predictors of the recognized pattern value10930, and the sensor learning circuit 10924 may instruct a continuedchange (e.g., while improvement is still occurring), an increased ordecreased rate of change (e.g., to converge more quickly on an improvedsensed parameter group 10928), and/or instruct a randomized change tothe sensed parameter group 10928 (e.g., to ensure that all potentiallyresult effective sensors are being checked, and/or to avoid converginginto a local optimal value).

Example and non-limiting options for the sensing performance value 10934include: a signal-to-noise performance for detecting a value of interestin the industrial system (e.g., a determination that the predictionsignal for the value is high relative to noise factors for one or moresensors of the sensed parameter group 10928, and/or for the sensedparameter group 10928 as a whole); a network utilization of the sensorsin the industrial system (e.g., the sensor learning circuit 10924 mayscore a sensed parameter group 10928 relatively high where it is aseffective or almost as effective as another sensed parameter group10928, but results in lower network utilization); an effective sensingresolution for a value of interest in the industrial system (e.g., thesensor learning circuit 10924 may score a sensed parameter group 10928relatively high where it provides a responsive prediction of the outputvalue to smaller changes in input values); a power consumption value fora sensing system in the industrial system, the sensing system includingthe sensors (e.g., the sensor learning circuit 10924 may score a sensedparameter group 10928 relatively high where it is as effective or almostas effective as another sensed parameter group 10928, but results inlower power consumption); a calculation efficiency for determining thesecondary value (e.g., the sensor learning circuit 10924 may score asensed parameter group 10928 relatively high where it is as effective oralmost as effective as another sensed parameter group 10928 indetermining the secondary value 10932, but results in fewer processorcycles, lower network utilization, and/or lower memory utilizationincluding stored memory requirements as well as intermediate memoryutilization such as buffers); an accuracy and/or a precision of thesecondary value (e.g., the sensor learning circuit 10924 may score asensed parameter group 10928 relatively high where it provides a highlyaccurate and/or highly precise determination of the secondary value10932); a redundancy capacity for determining the secondary value (e.g.,the sensor learning circuit 10924 may score a sensed parameter group10928 relatively high where it provides similar capability and/orresource utilization, but provides for additional sensing redundancy,such as being more robust to gaps in data from one or more of thesensors in the sensed parameter group 10928); and/or a lead time valuefor determining the secondary value 10932 (e.g., the sensor learningcircuit 10924 may score a sensed parameter group 10928 relatively highwhere it provides an improved or sufficient lead time in the secondaryvalue 10932 determination—for example to assist in avoidingover-temperature operation, spoiling an entire production run,determining whether a component has sufficient service life to completea production run, etc.) Example and non-limiting calculation efficiencyvalues include one or more determinations such as: processor operationsto determine the secondary value 10932; memory utilization fordetermining the secondary value 10932; a number of sensor inputs fromthe number of sensors for determining the secondary value 10932; and/orsupporting memory, such as long-term storage or buffers for supportingthe secondary value 10932.

Example systems include one or more, or all, of the sensors 10908 asanalog sensors and/or as remote sensors. An example system includes thesecondary value 10932 being a value such as: a virtual sensor outputvalue;

a process prediction value (e.g., a success value for a production run,an overtemperature value, an overpressure value, a product qualityvalue, etc.); a process state value (e.g., a stage of the process, atemperature at a time and location in the process); a componentprediction value (e.g., a component failure prediction, a componentmaintenance or service prediction, a component response to an operatingchange prediction); a component state value (a remaining service life ormaintenance interval for a component); and/or a model output valuehaving the sensor data values 10948 from the fused number of sensors10926 as an input. An example system includes the fused number ofsensors 10926 being one or more of the combinations of sensors such as:a vibration sensor and a temperature sensor; a vibration sensor and apressure sensor; a vibration sensor and an electric field sensor; avibration sensor and a heat flux sensor; a vibration sensor and agalvanic sensor; and/or a vibration sensor and a magnetic sensor.

An example sensor learning circuit 10924 further updates the sensedparameter group 10928 by performing an operation such as: updating asensor selection of the sensed parameter group 10928 (e.g., whichsensors are sampled); updating a sensor sampling rate of at least onesensor from the sensed parameter group (e.g., how fast the sensorsprovide information, and/or how fast information is passed through thenetwork); updating a sensor resolution of at least one sensor from thesensed parameter group (e.g., changing or requesting a change in asensor resolution, utilizing additional sensors to provide greatereffective resolution); updating a storage value corresponding to atleast one sensor from the sensed parameter group (e.g., storing datafrom the sensor at a higher or lower resolution, and/or over a longer orshorter time period); updating a priority corresponding to at least onesensor from the sensed parameter group (e.g., moving a sensor up to ahigher priority—for example, if environmental conditions prevent datareceipt from all planned sensors, and/or reducing a time lag betweencreation of the sensed data and receipt at the sensor learning circuit10924); and/or updating at least one of a sampling rate, sampling order,sampling phase, and/or a network path configuration corresponding to atleast one sensor from the sensed parameter group.

An example pattern recognition circuit 10922 further determines therecognized pattern value 10930 by performing an operation such as:determining a signal effectiveness of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to avalue of interest 10950 (e.g., determining that a sensor value is a goodpredictor of the value of interest 10950); determining a sensitivity ofat least one sensor of the sensed parameter group 10928 and the updatedsensed parameter group 10928 relative to the value of interest 10950(e.g., determining the relative sensitivity of the determined value ofinterest to small changes in operating conditions based on the selectedsensed parameter group 10928); determining a predictive confidence of atleast one sensor of the sensed parameter group 10928 and the updatedsensed parameter group 10928 relative to the value of interest 10950;determining a predictive delay time of at least one sensor of the sensedparameter group 10928 and the updated sensed parameter group 10928relative to the value of interest 10950; determining a predictiveaccuracy of at least one sensor of the sensed parameter group 10928 andthe updated sensed parameter group 10928 relative to the value ofinterest 10950; determining a classification precision of at least onesensor of the sensed parameter group 10928 (e.g., determining theaccuracy of classification of a pattern by a machine classifier based onuse of the at least one sensor); determining a predictive precision ofat least one sensor of the sensed parameter group 10928 and the updatedsensed parameter group 10928 relative to the value of interest 10950;and/or updating the recognized pattern value 10930 in response toexternal feedback, which may be received as external data 10952 (e.g.,where an outcome is known, such as a maintenance event, product qualitydetermination, production outcome determination, etc., the detection ofthe recognized pattern value 10930 is thereby improved according to theconditions of the system before the known outcome occurred). Example andnon-limiting values of interest 10950 include: a virtual sensor outputvalue; a process prediction value; a process state value; a componentprediction value; a component state value; and/or a model output valuehaving the sensor data values from the fused plurality of sensors as aninput.

An example pattern recognition circuit 10922 further accessescloud-based data 10954 including a second number of sensor data values,the second number of sensor data values corresponding to at least oneoffset industrial system. An example sensor learning circuit 10924further accesses the cloud-based data 10954 including a second updatedsensor parameter group corresponding to the at least one offsetindustrial system. Accordingly, the pattern recognition circuit 10922can improve pattern recognition in the system based on increasedstatistical data available from an offset system. Additionally, oralternatively, the sensor learning circuit 10924 can improve morerapidly and with greater confidence based upon the data from the offsetsystem—including determining which sensors in the offset system found tobe effective in predicting system outcomes.

Referencing FIG. 114, an example procedure 10936 for data collection inan industrial environment includes an operation 10938 to provide anumber of sensors to an industrial system including a number ofcomponents, each of the number of sensors operatively coupled to atleast one of the number of components. The procedure 10936 furtherincludes an operation 10940 to interpret a number of sensor data valuesin response to a sensed parameter group, the sensed parameter groupincluding a fused number of sensors from the number of sensors, anoperation 10942 to determine a recognized pattern value including asecondary value determined in response to the number of sensor datavalues, an operation 10944 to update the sensed parameter group inresponse to the recognized pattern value, and an operation 10946 toadjust the interpreting the number of sensor data values in response tothe updated sensed parameter group.

An example procedure 10936 includes an operation to iteratively performthe determining the recognized pattern value and the updating the sensedparameter group to improve a sensing performance value (e.g., byrepeating operations 10940 to 10944 periodically, at selected intervals,and/or in response to a system change). An example procedure 10936includes determining the sensing performance value by determining: asignal-to-noise performance for detecting a value of interest in theindustrial system; a network utilization of the plurality of sensors inthe industrial system; an effective sensing resolution for a value ofinterest in the industrial system; a power consumption value for asensing system in the industrial system, the sensing system includingthe plurality of sensors; a calculation efficiency for determining thesecondary value; an accuracy and/or a precision of the secondary value;a redundancy capacity for determining the secondary value; and/or a leadtime value for determining the secondary value.

An example procedure 10936 includes the operation 10944 to update thesensed parameter group by performing at least one operation such as:updating a sensor selection of the sensed parameter group; updating asensor sampling rate of at least one sensor from the sensed parametergroup; updating a sensor resolution of at least one sensor from thesensed parameter group; updating a storage value corresponding to atleast one sensor from the sensed parameter group; updating a prioritycorresponding to at least one sensor from the sensed parameter group;and/or updating at least one of a sampling rate, sampling order,sampling phase, and a network path configuration corresponding to atleast one sensor from the sensed parameter group. An example procedure10936 includes the operation 10942 to determine the recognized patternvalue by performing at least one operation such as: determining a signaleffectiveness of at least one sensor of the sensed parameter group andthe updated sensed parameter group relative to a value of interest;determining a sensitivity of at least one sensor of the sensed parametergroup and the updated sensed parameter group relative to the value ofinterest; determining a predictive confidence of at least one sensor ofthe sensed parameter group and the updated sensed parameter grouprelative to the value of interest; determining a predictive delay timeof at least one sensor of the sensed parameter group and the updatedsensed parameter group relative to the value of interest; determining apredictive accuracy of at least one sensor of the sensed parameter groupand the updated sensed parameter group relative to the value ofinterest; determining a predictive precision of at least one sensor ofthe sensed parameter group and the updated sensed parameter grouprelative to the value of interest; and/or updating the recognizedpattern value in response to external feedback.

Clause 1. In embodiments, a system for data collection in an industrialenvironment, the system comprising: an industrial system comprising aplurality of components, and a plurality of sensors each operativelycoupled to at least one of the plurality of components; a sensorcommunication circuit structured to interpret a plurality of sensor datavalues in response to a sensed parameter group; a pattern recognitioncircuit structured to determine a recognized pattern value in responseto a least a portion of the plurality of sensor data values; and asensor learning circuit structured to update the sensed parameter groupin response to the recognized pattern value; wherein the sensorcommunication circuit is further structured to adjust the interpretingof the plurality of sensor data values in response to the updated sensedparameter group. 2. The system of clause 1, wherein the sensed parametergroup comprises a fused plurality of sensors, and wherein the recognizedpattern value further includes a secondary value comprising a valuedetermined in response to the fused plurality of sensors. 3. The systemof clause 2, wherein the pattern recognition circuit and sensor learningcircuit are further structured to iteratively perform the determiningthe recognized pattern value and the updating the sensed parameter groupto improve a sensing performance value. 4. The system of clause 3,wherein the sensing performance value comprises at least one performancedetermination selected from the performance determinations consistingof: a signal-to-noise performance for detecting a value of interest inthe industrial system; a network utilization of the plurality of sensorsin the industrial system; an effective sensing resolution for a value ofinterest in the industrial system; and a power consumption value for asensing system in the industrial system, the sensing system includingthe plurality of sensors. 5. The system of clause 3, wherein the sensingperformance value comprises a signal-to-noise performance for detectinga value of interest in the industrial system. 6. The system of clause 3,wherein the sensing performance value comprises a network utilization ofthe plurality of sensors in the industrial system. 7. The system ofclause 3, wherein the sensing performance value comprises an effectivesensing resolution for a value of interest in the industrial system. 8.The system of clause 3, wherein the sensing performance value comprisesa power consumption value for a sensing system in the industrial system,the sensing system including the plurality of sensors. 9. The system ofclause 3, wherein the sensing performance value comprises a calculationefficiency for determining the secondary value. 10 The system of clause9, wherein the calculation efficiency comprises at least one of:processor operations to determine the secondary value, memoryutilization for determining the secondary value, a number of sensorinputs from the plurality of sensors for determining the secondaryvalue, and supporting data long-term storage for supporting thesecondary value. 11. The system of clause 3, wherein the sensingperformance value comprises one of an accuracy and a precision of thesecondary value. 12. The system of clause 3, wherein the sensingperformance value comprises a redundancy capacity for determining thesecondary value. 13. The system of clause 3, wherein the sensingperformance value comprises a lead time value for determining thesecondary value. 14. The system of clause 13, wherein the secondaryvalue comprises a component overtemperature value. 15. The system ofclause 13, wherein the secondary value comprises one of a componentmaintenance time, a component failure time, and a component servicelife. 16. The system of clause 13, wherein the secondary value comprisesan off nominal operating condition affecting a product quality producedby an operation of the industrial system. 17. The system of clause 1,wherein the plurality of sensors comprises at least one analog sensor.18. The system of clause 1, wherein at least one of the sensorscomprises a remote sensor. 19. The system of clause 2, wherein thesecondary value comprises at least one value selected from the valuesconsisting of: a virtual sensor output value; a process predictionvalue; a process state value; a component prediction value; a componentstate value; and a model output value having the sensor data values fromthe fused plurality of sensors as an input. 20. The system of clause 2,wherein the fused plurality of sensors further comprises at least onepairing of sensor types selected from the pairings consisting of: avibration sensor and a temperature sensor; a vibration sensor and apressure sensor; a vibration sensor and an electric field sensor; avibration sensor and a heat flux sensor; a vibration sensor and agalvanic sensor; and a vibration sensor and a magnetic sensor. 21. Thesystem of clause 1, wherein the sensor learning circuit is furtherstructured to update the sensed parameter group by performing at leastone operation selected from the operations consisting of: updating asensor selection of the sensed parameter group; updating a sensorsampling rate of at least one sensor from the sensed parameter group;updating a sensor resolution of at least one sensor from the sensedparameter group; updating a storage value corresponding to at least onesensor from the sensed parameter group; updating a prioritycorresponding to at least one sensor from the sensed parameter group;and updating at least one of a sampling rate, sampling order, samplingphase, and a network path configuration corresponding to at least onesensor from the sensed parameter group. 22. The system of clause 21,wherein the pattern recognition circuit is further structured todetermine the recognized pattern value by performing at least oneoperation selected from the operations consisting of: determining asignal effectiveness of at least one sensor of the sensed parametergroup and the updated sensed parameter group relative to a value ofinterest; determining a sensitivity of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to thevalue of interest; determining a predictive confidence of at least onesensor of the sensed parameter group and the updated sensed parametergroup relative to the value of interest; determining a predictive delaytime of at least one sensor of the sensed parameter group and theupdated sensed parameter group relative to the value of interest;determining a predictive accuracy of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to thevalue of interest; determining a predictive precision of at least onesensor of the sensed parameter group and the updated sensed parametergroup relative to the value of interest; and updating the recognizedpattern value in response to external feedback. 23. The system of clause22, wherein the value of interest comprises at least one value selectedfrom the values consisting of: a virtual sensor output value; a processprediction value; a process state value; a component prediction value; acomponent state value; and a model output value having the sensor datavalues from the fused plurality of sensors as an input. 24. The systemof clause 2, wherein the pattern recognition circuit is furtherstructured to access cloud-based data comprising a second plurality ofsensor data values, the second plurality of sensor data valuescorresponding to at least one offset industrial system. 25. The systemof clause 24, wherein the sensor learning circuit is further structuredto access the cloud-based data comprising a second updated sensorparameter group corresponding to the at least one offset industrialsystem. 26. A method, comprising: providing a plurality of sensors to anindustrial system comprising a plurality of components, each of theplurality of sensors operatively coupled to at least one of theplurality of components; interpreting a plurality of sensor data valuesin response to a sensed parameter group, the sensed parameter groupcomprising a fused plurality of sensors from the plurality of sensors;determining a recognized pattern value comprising a secondary valuedetermined in response to the plurality of sensor data values; updatingthe sensed parameter group in response to the recognized pattern value;and adjusting the interpreting the plurality of sensor data values inresponse to the updated sensed parameter group. 27. The method of clause26, further comprising iteratively performing the determining therecognized pattern value and the updating the sensed parameter group toimprove a sensing performance value. 28. The method of clause 27,further comprising determining the sensing performance value in responseto determining at least one of: a signal-to-noise performance fordetecting a value of interest in the industrial system; a networkutilization of the plurality of sensors in the industrial system;

an effective sensing resolution for a value of interest in theindustrial system; a power consumption value for a sensing system in theindustrial system, the sensing system including the plurality ofsensors; a calculation efficiency for determining the secondary value,wherein the calculation efficiency comprises at least one of: processoroperations to determine the secondary value, memory utilization fordetermining the secondary value, a number of sensor inputs from theplurality of sensors for determining the secondary value, and supportingdata long-term storage for supporting the secondary value; one of anaccuracy and a precision of the secondary value; a redundancy capacityfor determining the secondary value; and a lead time value fordetermining the secondary value. 29. The method of clause 27, whereinupdating the sensed parameter group comprises performing at least oneoperation selected from the operations consisting of: updating a sensorselection of the sensed parameter group; updating a sensor sampling rateof at least one sensor from the sensed parameter group; updating asensor resolution of at least one sensor from the sensed parametergroup; updating a storage value corresponding to at least one sensorfrom the sensed parameter group; updating a priority corresponding to atleast one sensor from the sensed parameter group; and updating at leastone of a sampling rate, sampling order, sampling phase, and a networkpath configuration corresponding to at least one sensor from the sensedparameter group. 30. The method of clause 27, wherein determining therecognized pattern value comprises performing at least one operationselected from the operations consisting of: determining a signaleffectiveness of at least one sensor of the sensed parameter group andthe updated sensed parameter group relative to a value of interest;determining a sensitivity of at least one sensor of the sensed parametergroup and the updated sensed parameter group relative to the value ofinterest; determining a predictive confidence of at least one sensor ofthe sensed parameter group and the updated sensed parameter grouprelative to the value of interest; determining a predictive delay timeof at least one sensor of the sensed parameter group and the updatedsensed parameter group relative to the value of interest; determining apredictive accuracy of at least one sensor of the sensed parameter groupand the updated sensed parameter group relative to the value ofinterest; determining a predictive precision of at least one sensor ofthe sensed parameter group and the updated sensed parameter grouprelative to the value of interest; and updating the recognized patternvalue in response to external feedback. 31. A system for data collectionin an industrial environment, the system comprising: an industrialsystem comprising a plurality of components, and a plurality of sensorseach operatively coupled to at least one of the plurality of components;a sensor communication circuit structured to interpret a plurality ofsensor data values in response to a sensed parameter group, wherein thesensed parameter group comprises a fused plurality of sensors; a meansfor recognizing a pattern value in response to the sensed parametergroup; and a means for updating the sensed parameter group in responseto the recognized pattern value. 32. The system of clause 31, furthercomprising a means for iteratively updating the sensed parameter group.33. The system of clause 32, further comprising a means for accessing atleast one of external data and a second plurality of sensor data valuescorresponding to an offset industrial system, and wherein the means foriteratively updating the sensed parameter group is further responsive tothe at least one of external data and the second plurality of sensordata values. 34. The system of clause 33, further comprising a means foraccessing a second sensed parameter group corresponding to the offsetindustrial system, and wherein the means for iteratively updating isfurther responsive to the second sensed parameter group. 35. A systemfor data collection in an industrial environment, the system comprising:an industrial system comprising a plurality of components, and aplurality of sensors each operatively coupled to at least one of theplurality of components; a sensor communication circuit structured tointerpret a plurality of sensor data values in response to a sensedparameter group; a pattern recognition circuit structured to determine arecognized pattern value in response to a least a portion of theplurality of sensor data values, wherein the recognized pattern valueincludes a secondary value comprising a value determined in response tothe at least a portion of the plurality of sensors; a sensor learningcircuit structured to update the sensed parameter group in response tothe recognized pattern value; wherein the sensor communication circuitis further structured to adjust the interpreting the plurality of sensordata values in response to the updated sensed parameter group; andwherein the pattern recognition circuit and the sensor learning circuitare further structured to iteratively perform the determining therecognized pattern value and the updating the sensed parameter group toimprove a sensing performance value, wherein the sensing performancevalue comprises a signal-to-noise performance for detecting a value ofinterest in the industrial system. 36. The system of clause 35, whereinthe sensed parameter group comprises a fused plurality of sensors, andwherein the secondary value comprises a value determined in response tothe fused plurality of sensors. 37. The system of clause 36, wherein thesecondary value comprises at least one value selected from the valuesconsisting of: a virtual sensor output value; a process predictionvalue; a process state value; a component prediction value; a componentstate value; and a model output value having the sensor data values fromthe fused plurality of sensors as an input. 38. A system for datacollection in an industrial environment, the system comprising: anindustrial system comprising a plurality of components, and a pluralityof sensors each operatively coupled to at least one of the plurality ofcomponents; a sensor communication circuit structured to interpret aplurality of sensor data values in response to a sensed parameter group;a pattern recognition circuit structured to determine a recognizedpattern value in response to a least a portion of the plurality ofsensor data values, wherein the recognized pattern value includes asecondary value comprising a value determined in response to the atleast a portion of the plurality of sensors; a sensor learning circuitstructured to update the sensed parameter group in response to therecognized pattern value; wherein the sensor communication circuit isfurther structured to adjust the interpreting the plurality of sensordata values in response to the updated sensed parameter group; andwherein the pattern recognition circuit and the sensor learning circuitare further structured to iteratively perform the determining therecognized pattern value and the updating the sensed parameter group toimprove a sensing performance value, wherein the sensing performancevalue comprises a network utilization of the plurality of sensors in theindustrial system. 39. The system of clause 37, wherein the sensedparameter group comprises a fused plurality of sensors, and wherein thesecondary value comprises a value determined in response to the fusedplurality of sensors. 40. The system of clause 39, wherein the secondaryvalue comprises at least one value selected from the values consistingof: a virtual sensor output value; a process prediction value; a processstate value; a component prediction value; a component state value; anda model output value having the sensor data values from the fusedplurality of sensors as an input. 41. A system for data collection in anindustrial environment, the system comprising: an industrial systemcomprising a plurality of components, and a plurality of sensors eachoperatively coupled to at least one of the plurality of components; asensor communication circuit structured to interpret a plurality ofsensor data values in response to a sensed parameter group; a patternrecognition circuit structured to determine a recognized pattern valuein response to a least a portion of the plurality of sensor data values,wherein the recognized pattern value includes a secondary valuecomprising a value determined in response to the at least a portion ofthe plurality of sensors; a sensor learning circuit structured to updatethe sensed parameter group in response to the recognized pattern value;wherein the sensor communication circuit is further structured to adjustthe interpreting the plurality of sensor data values in response to theupdated sensed parameter group; and wherein the pattern recognitioncircuit and the sensor learning circuit are further structured toiteratively perform the determining the recognized pattern value and theupdating the sensed parameter group to improve a sensing performancevalue, wherein the sensing performance value comprises an effectivesensing resolution for a value of interest in the industrial system. 42.The system of clause 41, wherein the sensed parameter group comprises afused plurality of sensors, and wherein the secondary value comprises avalue determined in response to the fused plurality of sensors. 43. Thesystem of clause 42, wherein the secondary value comprises at least onevalue selected from the values consisting of: a virtual sensor outputvalue; a process prediction value; a process state value; a componentprediction value; a component state value; and a model output valuehaving the sensor data values from the fused plurality of sensors as aninput. 44. A system for data collection in an industrial environment,the system comprising: an industrial system comprising a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group; a pattern recognition circuit structured todetermine a recognized pattern value in response to a least a portion ofthe plurality of sensor data values, wherein the recognized patternvalue includes a secondary value comprising a value determined inresponse to the at least a portion of the plurality of sensors; a sensorlearning circuit structured to update the sensed parameter group inresponse to the recognized pattern value; wherein the sensorcommunication circuit is further structured to adjust the interpretingthe plurality of sensor data values in response to the updated sensedparameter group; and wherein the pattern recognition circuit and thesensor learning circuit are further structured to iteratively performthe determining the recognized pattern value and the updating the sensedparameter group to improve a sensing performance value, wherein thesensing performance value comprises a power consumption value for asensing system in the industrial system, the sensing system includingthe plurality of sensors. 45. The system of clause 44, wherein thesensed parameter group comprises a fused plurality of sensors, andwherein the secondary value comprises a value determined in response tothe fused plurality of sensors. 46. The system of clause 45, wherein thesecondary value comprises at least one value selected from the valuesconsisting of: a virtual sensor output value; a process predictionvalue; a process state value; a component prediction value; a componentstate value; and a model output value having the sensor data values fromthe fused plurality of sensors as an input.

Referencing FIG. 115, an example system 11000 for data collection in anindustrial environment includes an industrial system 11002 having anumber of components 11004, and a number of sensors 11006 eachoperatively coupled to at least one of the number of components 11004.The selection, distribution, type, and communicative setup of sensorsdepends upon the application of the system 11000 and/or the context.

The example system 11000 further includes a sensor communication circuit11018 (reference FIG. 116) that interprets a number of sensor datavalues 11034 in response to a sensed parameter group 11026. The sensedparameter group 11026 includes a description of which sensors 11006 aresampled at which times, including at least the selected samplingfrequency, a process stage wherein a particular sensor may be providinga value of interest, and the like. An example system includes the sensedparameter group 11026 being a number of sensors provided for a sensorfusion operation. In certain embodiments, the sensed parameter group11026 includes a set of sensors that encompass detection of operatingconditions of the system that predict outcomes, off-nominal operations,maintenance intervals, maintenance health states, and/or future statevalues for any of these, for a process, a component, a sensor, and/orany aspect of interest for the system 11000.

In certain embodiments, sensor data values 11034 are provided to a datacollector 11008, which may be in communication with multiple sensors11006 and/or with a controller 11012. In certain embodiments, a plantcomputer 11010 is additionally or alternatively present. In the examplesystem, the controller 11012 is structured to functionally executeoperations of the sensor communication circuit 11018, patternrecognition circuit 11020, and/or the system characterization circuit11022, and is depicted as a separate device for clarity of description.Aspects of the controller 11012 may be present on the sensors 11006, thedata collector 11008, the plant computer 11010, and/or on a cloudcomputing device 11014. In certain embodiments, all aspects of thecontroller 11012 may be present in another device depicted on the system11000. The plant computer 11010 represents local computing resources,for example processing, memory, and/or network resources, that may bepresent and/or in communication with the industrial system 11000. Incertain embodiments, the cloud computing device 11014 representscomputing resources externally available to the industrial system 11000,for example over a private network, intra-net, through cellularcommunications, satellite communications, and/or over the internet. Incertain embodiments, the data collector 11008 may be a computing device,a smart sensor, a MUX box, or other data collection device capable toreceive data from multiple sensors and to pass-through the data and/orstore data for later transmission. An example data collector 11008 hasno storage and/or limited storage, and selectively passes sensor datatherethrough, with a subset of the sensor data being communicated at agiven time due to bandwidth considerations of the data collector 11008,a related network, and/or imposed by environmental constraints. Incertain embodiments, one or more sensors and/or computing devices in thesystem 11000 are portable devices—for example a plant operator walkingthrough the industrial system may have a smart phone, which the system11000 may selectively utilize as a data collector 11008, sensor11006—for example to enhance communication throughput, sensorresolution, and/or as a primary method for communicating sensor datavalues 11034 to the controller 11012.

The example system 11000 further includes a pattern recognition circuit11020 that determines a recognized pattern value 11028 in response to aleast a portion of the sensor data values 11034, and a systemcharacterization circuit 11022 that provides a system characterizationvalue 11030 for the industrial system in response to the recognizedpattern value 11028. The system characterization value 11030 includesany value determined from the pattern recognition operations of thepattern recognition circuit 11020, including determining that a systemcondition of interest is present, a component condition of interest ispresent, an abstracted condition of the system or a component is present(e.g., a product quality value; an operation cost value; a componenthealth, wear, or maintenance value; a component capacity value; and/or asensor saturation value) and/or is predicted to occur within a timeframe (e.g., calendar time, operational time, and/or a process stage) ofinterest. Pattern recognition operations include determining thatoperations compatible with a previously known pattern, operationssimilar to a previously known pattern and/or extrapolated frompreviously known pattern information (e.g., a previously known patternincludes a temperature response for a first component, and a known orestimated relationship between components allows for a determinationthat a temperature for a second component will exceed a threshold basedupon the pattern recognition for the first component combined with theknown or estimated relationship).

Non-limiting descriptions of a number of examples of a systemcharacterization value 11030 are described following. An example systemcharacterization value 11030 includes a predicted outcome for a processassociated with the industrial system—for example a product qualitydescription, a product quantity description, a product variabilitydescription (e.g., the expected variability of a product parameterpredicted according to the operating conditions of the system), aproduct yield description, a net present value (NPV) for a process, aprocess completion time, a process chance of completion success, and/ora product purity result. The predicted outcome may be a batch prediction(e.g., a single run, or an integer number of runs, of the process, andthe associated predicted outcome), a time based prediction (e.g., theprojected outcome of the process over the next day, the next threeweeks, until a scheduled shutdown, etc.), a production definedprediction (e.g., the projected outcome over the next 1,000 units, overthe next 47 orders, etc.), and/or a rate of change based outcome (e.g.,projected for 3 component failures per month, an emissions output peryear, etc.). An example system characterization value 11030 includes apredicted future state for a process associated with the industrialsystem—for example an operating temperature at a given future time, anenergy consumption value, a volume in a tank, an emitted noise value ata school adjacent to the industrial system, and/or a rotational speed ofa pump. The predicted future state may be time based (e.g., at 4 PM onThursday), based on a state of the process (e.g., during the thirdstage, during system shutdown, etc.), and/or based on a future state ofparticular interest (e.g., peak energy consumption, highest temperaturevalue, maximum noise value, time or process stage when a maximum numberof personnel will be within 50 feet of a sensitive area, time or processstage when an aspect of the system redundancy is at a lowest point—e.g.,for determining high risk points in a process, etc.). An example systemcharacterization value 11030 includes a predicted off-nominal operationfor the process associated with the industrial system—for example when acomponent capacity of the system will exceed nominal parameters(although, possibly, not experience a failure), when any parameter inthe system will be three standard deviations away from normaloperations, when a capacity of a component will be under-utilized, etc.An example system characterization value 11030 includes a predictionvalue for one of the number of components—for example an operatingcondition at a point in time and/or process stage. An example systemcharacterization value 11030 includes a future state value for one ofthe number of components. The predicted future state of a component maybe time based, based on a state of the process, and/or based on a futurestate of particular interest (e.g., a highest or lowest value predictedfor the component). An example system characterization value 11030includes an anticipated maintenance health state information for one ofthe number of components, including at a particular time, a processstage, a lowest value predicted until a next maintenance event, etc. Anexample system characterization value 11030 includes a predictedmaintenance interval for at least one of the number of components (e.g.,based on current usage, anticipated usage, planned process operations,etc.). An example system characterization value 11030 includes apredicted off-nominal operation for one of the number of components—forexample at a selected time, a process stage, and/or a future state ofparticular interest. An example system characterization value 11030includes a predicted fault operation for one of the plurality ofcomponents—for example at a selected time, a process stage, any faultoccurrence predicted based on current usage, anticipated usage, plannedprocess operations, and/or a future state of particular interest. Anexample system characterization value 11030 includes a predictedexceedance value for one of the number of components, where theexceedance value includes exceedance of a design specification, and/orexceedance of a selected threshold. An example system characterizationvalue 11030 includes a predicted saturation value for one of theplurality of sensors for example at a selected time, a process stage,any saturation occurrence predicted based on current usage, anticipatedusage, planned process operations, and/or a future state of particularinterest.

Any values for the prediction value 11030 may be raw values (e.g., atemperature value), derivative values (e.g., a rate of change of atemperature value), accumulated values (e.g., a time spent above one ormore temperature thresholds) including weighted accumulated values,and/or integrated values (e.g., an area over a temperature-time curve ata temperature value or temperature trajectory of interest). The providedexamples list temperature, but any prediction value 11030 may beutilized, including at least vibration, system throughput, pressure,etc. In certain embodiments, combinations of one or more predictionvalues 11030 may be utilized.

It will be appreciated in light of the disclosure that combiningprediction values 11030 can create particularly powerful combinationsfor system analysis, control, and risk management, which arespecifically contemplated herein. For example, a first prediction valuemay indicate a time or process stage for a maximum flow rate through thesystem, and a second prediction value may determine the predicted stateof one or more components of the system that is present at thatparticular time or process stage. In another example, a first predictionvalue indicates a lowest margin of the system in terms of capacity todeliver (e.g., by determining a point in the process wherein at leastone component has a lowest operating margin, and/or where a group ofcomponents have a statistically lower operating margin due to the riskinduced by a number of simultaneous low operating margins), and a secondprediction value testing a system risk (e.g., loss of inlet water, lossof power, increase in temperature, change in environmental conditionsthat reduce or increase heat transfer, or that preclude the emission ofcertain effluents), and the combined risk of separate events can beassessed on the total system risk. Additionally, the prediction valuesmay be operated with a sensitivity check (e.g., varying systemconditions within margins to determine if some failure may occur),wherein the use of the prediction value allows for the sensitivity checkto be performed with higher resolution at high risk points in theprocess.

An example system 11000 further includes a system collaboration circuit11024 that interprets external data 11036, and where the patternrecognition circuit 11020 further determines the recognized patternvalue 11028 further in response to the external data 11036. Externaldata 11036 includes, without limitation, data provided from outside thesystem 11000 and/or outside the controller 11012. Non-limiting exampleexternal data 11036 include entries from an operator (e.g., indicating afailure, a fault, and/or a service event). An example patternrecognition circuit 11020 further iteratively improves patternrecognition operations in response to the external data 11036 (e.g.,where an outcome is known, such as a maintenance event, product qualitydetermination, production outcome determination, etc., the detection ofthe recognized pattern value 11028 is thereby improved according to theconditions of the system before the known outcome occurred). Example andnon-limiting external data 11036 includes data such as: an indicatedprocess success value; an indicated process failure value; an indicatedcomponent maintenance event; an indicated component failure event; anindicated process outcome value; an indicated component wear value; anindicated process operational exceedance value; an indicated componentoperational exceedance value; an indicated fault value; and/or anindicated sensor saturation value.

An example system 11000 further includes a system collaboration circuit11024 that interprets cloud-based data 11032 including a second numberof sensor data values, the second number of sensor data valuescorresponding to at least one offset industrial system, and where thepattern recognition circuit 11020 further determines the recognizedpattern value 11028 further in response to the cloud-based data 11032.An example pattern recognition circuit 11020 further iterativelyimproves pattern recognition operations in response to the cloud-baseddata 11032. An example sensed parameter group 11026 includes a triaxialvibration sensor, a vibration sensor and a second sensor that is not avibration sensor, the second sensor being a digital sensor, and/or anumber of analog sensors.

Referencing FIG. 117, an example procedure 11038 includes an operation11040 to provide a number of sensors to an industrial system including anumber of components, each of the number of sensors operatively coupledto at least one of the number of components, an operation 11042 tointerpret a number of sensor data values in response to a sensedparameter group, the sensed parameter group including at least onesensor of the number of sensors, an operation 11044 to determine arecognized pattern value in response to a least a portion of the numberof sensor data values, and an operation 11046 to provide a systemcharacterization value for the industrial system in response to therecognized pattern value.

An example procedure 11038 further includes the operation 11046 toprovide the system characterization value by performing an operationsuch as: determining a predicted outcome for a process associated withthe industrial system; determining a predicted future state for aprocess associated with the industrial system; determining a predictedoff-nominal operation for the process associated with the industrialsystem; determining a prediction value for one of the plurality ofcomponents; determining a future state value for one of the plurality ofcomponents; determining an anticipated maintenance health stateinformation for one of the plurality of components; determining apredicted maintenance interval for at least one of the plurality ofcomponents; determining a predicted off-nominal operation for one of theplurality of components; determining a predicted fault operation for oneof the plurality of components; determining a predicted exceedance valuefor one of the plurality of components; and/or determining a predictedsaturation value for one of the plurality of sensors.

An example procedure 11038 includes an operation 11050 to interpretexternal data and/or cloud-based data, and where the operation 11044 todetermine the recognized pattern value is further in response to theexternal data and/or the cloud-based data. An example procedure 11038includes an operation to iteratively improve pattern recognitionoperations in response to the external data and/or the cloud-based data,for example by operation 11048 to adjust the operation 11042interpreting sensor values, such as by updating the sensed parametergroup. The operation to iteratively improve pattern recognition mayfurther include repeating operations 11042 through 11048, periodically,at selected intervals, in response to a system change, and/or inresponse to a prediction value of a component, process, or the system.

In embodiments, a system for data collection in an industrialenvironment may comprise: an industrial system comprising a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a pattern recognition circuitstructured to determine a recognized pattern value in response to aleast a portion of the plurality of sensor data values; and a systemcharacterization circuit structured to provide a system characterizationvalue for the industrial system in response to the recognized patternvalue. In embodiments, a characterization value may include at least onecharacterization value selected from the characterization valuesconsisting of: a predicted outcome for a process associated with theindustrial system; a predicted future state for a process associatedwith the industrial system; and a predicted off-nominal operation forthe process associated with the industrial system. The systemcharacterization value may include at least one characterization valueselected from the characterization values consisting of: a predictionvalue for one of the plurality of components; a future state value forone of the plurality of components; an anticipated maintenance healthstate information for one of the plurality of components; and apredicted maintenance interval for at least one of the plurality ofcomponents. The system characterization value may include at least onecharacterization value selected from the characterization valuesconsisting of: a predicted off-nominal operation for one of theplurality of components; a predicted fault operation for one of theplurality of components; and a predicted exceedance value for one of theplurality of components. The system characterization value may include apredicted saturation value for one of the plurality of sensors. A systemcollaboration circuit may be included that is structured to interpretexternal data, and wherein the pattern recognition circuit is furtherstructured to determine the recognized pattern value further in responseto the external data. The pattern recognition circuit may be furtherstructured to iteratively improve pattern recognition operations inresponse to the external data. The external data may include at leastone of: an indicated component maintenance event; an indicated componentfailure event; an indicated component wear value; an indicated componentoperational exceedance value; and an indicated fault value. The externaldata may include at least one of: an indicated process failure value; anindicated process success value; an indicated process outcome value; andan indicated process operational exceedance value. The external data mayinclude an indicated sensor saturation value. A system collaborationcircuit may be included that is structured to interpret cloud-based datacomprising a second plurality of sensor data values, the secondplurality of sensor data values corresponding to at least one offsetindustrial system, and wherein the pattern recognition circuit isfurther structured to determine the recognized pattern value further inresponse to the cloud-based data. The pattern recognition circuit may befurther structured to iteratively improve pattern recognition operationsin response to the cloud-based data. The sensed parameter group mayinclude a triaxial vibration sensor. The sensed parameter group mayinclude a vibration sensor and a second sensor that is not a vibrationsensor, such as where the second sensor comprises a digital sensor. Thesensed parameter group may include a plurality of analog sensors.

In embodiments, a method may comprise: providing a plurality of sensorsto an industrial system comprising a plurality of components, each ofthe plurality of sensors operatively coupled to at least one of theplurality of components; interpreting a plurality of sensor data valuesin response to a sensed parameter group, the sensed parameter groupcomprising at least one sensor of the plurality of sensors; determininga recognized pattern value in response to a least a portion of theplurality of sensor data values; and providing a system characterizationvalue for the industrial system in response to the recognized patternvalue. The system characterization value may be provided by performingat least one operation selected from the operations consisting of:determining a prediction value for one of the plurality of components;determining a future state value for one of the plurality of components;determining an anticipated maintenance health state information for oneof the plurality of components; and determining a predicted maintenanceinterval for at least one of the plurality of components. The systemcharacterization value may be provided by performing at least oneoperation selected from the operations consisting of: determining apredicted outcome for a process associated with the industrial system;determining a predicted future state for a process associated with theindustrial system; and determining a predicted off-nominal operation forthe process associated with the industrial system. The systemcharacterization value may be provided by performing at least oneoperation selected from the operations consisting of: determining apredicted off-nominal operation for one of the plurality of components;determining a predicted fault operation for one of the plurality ofcomponents; and determining a predicted exceedance value for one of theplurality of components. The system characterization value may beprovided by determining a predicted saturation value for one of theplurality of sensors. Determining the recognized pattern value may befurther in response to the external data. Iteratively improving patternrecognition operations may be provided in response to the external data.Interpreting the external data may include at least one operationselected from the operations consisting of: interpreting an indicatedcomponent maintenance event; interpreting an indicated component failureevent; interpreting an indicated component wear value; interpreting anindicated component operational exceedance value; and interpreting anindicated fault value. Interpreting the external data may include atleast one operation selected from the operations consisting of:interpreting an indicated process success value; interpreting anindicated process failure value; interpreting an indicated processoutcome value; and interpreting an indicated process operationalexceedance value. Interpreting the external data may includeinterpreting an indicated sensor saturation value. Interpretingcloud-based data may include a second plurality of sensor data values,the second plurality of sensor data values corresponding to at least oneoffset industrial system, and wherein determining the recognized patternvalue is further in response to the cloud-based data. Iterativelyimproving pattern recognition operations may be provided in response tothe cloud-based data.

In embodiments, a system for data collection in an industrialenvironment may comprise: an industrial system comprising a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a means for determining arecognized pattern value in response to at least a portion of theplurality of sensor data values; and a means for providing a systemcharacterization value for the industrial system in response to therecognized pattern value. The means for providing the systemcharacterization value may comprise a means for performing at least oneoperation selected from the operations consisting of: determining apredicted outcome for a process associated with the industrial system;determining a predicted future state for a process associated with theindustrial system; and determining a predicted off-nominal operation forthe process associated with the industrial system. The means forproviding the system characterization value may include a means forperforming at least one operation selected from the operationsconsisting of: determining a prediction value for one of the pluralityof components; determining a future state value for one of the pluralityof components; determining an anticipated maintenance health stateinformation for one of the plurality of components; and determining apredicted maintenance interval for at least one of the plurality ofcomponents. The means for providing the system characterization valuemay include a means for performing at least one operation selected fromthe operations consisting of: determining a predicted off-nominaloperation for one of the plurality of components; determining apredicted fault operation for one of the plurality of components; anddetermining a predicted exceedance value for one of the plurality ofcomponents. The means for providing the system characterization valuemay include a means for determining a predicted saturation value for oneof the plurality of sensors. A system collaboration circuit may beprovided that is structured to interpret external data, and wherein themeans for determining the recognized pattern value determines therecognized pattern value further in response to the external data. Ameans for iteratively improving pattern recognition operations may beprovided in response to the external data. The external data may includeat least one of: an indicated process success value; an indicatedprocess failure value; and an indicated process outcome value. Theexternal data may include at least one of: an indicated componentmaintenance event; an indicated component failure event; and anindicated component wear value. The external data may include at leastone of: an indicated process operational exceedance value; an indicatedcomponent operational exceedance value; and an indicated fault value.The external data may include an indicated sensor saturation value. Asystem collaboration circuit may be provided that is structured tointerpret cloud-based data comprising a second plurality of sensor datavalues, the second plurality of sensor data values corresponding to atleast one offset industrial system, and wherein the means fordetermining the recognized pattern value determines the recognizedpattern value further in response to the cloud-based data. A means foriteratively improving pattern recognition operations may be provided inresponse to the cloud-based data.

In embodiments, a system for data collection in an industrialenvironment may comprise: a distillation column comprising a pluralityof components, and a plurality of sensors each operatively coupled to atleast one of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a pattern recognition circuitstructured to determine a recognized pattern value in response to aleast a portion of the plurality of sensor data values; and a systemcharacterization circuit structured to provide a system characterizationvalue for the distillation column in response to the recognized patternvalue. The plurality of components may include a thermodynamic treatmentcomponent, and wherein the system characterization value comprises atleast one value selected from the values consisting of: determining aprediction value for the thermodynamic treatment component; determininga future state value for the thermodynamic treatment component;determining an anticipated maintenance health state information for thethermodynamic treatment component; and determining a process ratelimitation according to a capacity of the thermodynamic treatmentcomponent. The thermodynamic treatment component may include at leastone of a compressor or a boiler.

In embodiments, a system for data collection in an industrialenvironment may comprise: a chemical process system comprising aplurality of components, and a plurality of sensors each operativelycoupled to at least one of the plurality of components; a sensorcommunication circuit structured to interpret a plurality of sensor datavalues in response to a sensed parameter group, the sensed parametergroup comprising at least one sensor of the plurality of sensors; apattern recognition circuit structured to determine a recognized patternvalue in response to a least a portion of the plurality of sensor datavalues; and a system characterization circuit structured to provide asystem characterization value for the chemical process system inresponse to the recognized pattern value. The chemical process systemmay include one of a chemical plant, a pharmaceutical plant, or an oilrefinery. The system characterization value may include at least onevalue selected from the values consisting of: a separation process valuecomprising at least one of a capacity value or a purity value; a sidereaction process value comprising a side reaction rate value; and athermodynamic treatment value comprising one of a capability, acapacity, and an anticipated maintenance health for a thermodynamictreatment component.

A system for data collection in an industrial environment, the systemcomprising:

an irrigation system comprising a plurality of components including apump, and a plurality of sensors each operatively coupled to at leastone of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a pattern recognition circuitstructured to determine a recognized pattern value in response to aleast a portion of the plurality of sensor data values; and a systemcharacterization circuit structured to provide a system characterizationvalue for the irrigation system in response to the recognized patternvalue. The system characterization value may include at least one of ananticipated maintenance health value for the pump and a future statevalue for the pump. The pattern recognition circuit may determine anoff-nominal process condition in response to the at least a portion ofthe plurality of sensor data values, and wherein the sensorcommunication circuit is further structured to change the sensedparameter group in response to the off-nominal process condition. Theoff-nominal process condition may include an indication of below normalwater feed availability, and wherein the updated sensed parameter groupcomprises at least one sensor selected from the sensors consisting of: awater level sensor, a humidity sensor, and an auxiliary water levelsensor.

As described elsewhere herein, feedback to various intelligent and/orexpert systems, control systems (including remote and local systems,autonomous systems, and the like), and the like, which may compriserule-based systems, model-based systems, artificial intelligence (AI)systems (including neural nets, self-organizing systems, and othersdescribed throughout this disclosure), and various combinations andhybrids of those (collectively referred to herein as the “expert system”except where context indicates otherwise), may include a wide range ofinformation, including measures such as utilization measures, efficiencymeasures (e.g., power, financial such as reduction of costs), measuresof success in prediction or anticipation of states (e.g., avoidance andmitigation of faults), productivity measures (e.g., workflow), yieldmeasures, profit measures, and the like, as described herein. Inembodiments feedback to the expert system may be industry-specific,domain-specific, factory-specific, machine-specific and the like.

Industry-specific feedback for the expert system may be offered by athird party, such as a repair and maintenance organization,manufacturer, one or more consortia, and the like, or may be generatedby one or more elements of the subject system itself. Industry-specificfeedback may be aggregated, such as into one or more data structures,wherein the data are aggregated at the component level, equipment level,factory/installation level, and/or industry level. Users of the datastructure(s) may access data at any level (e.g., component, equipment,factory, industry, etc.) Users may search the data structure(s) forindicators/predictors based on or filtered by system conditions specificto their need, or update an indicator/predictor with proprietary data tocustomize the data structure to their industry. In embodiments, theexpert system may be seeded with industry-specific feedback, such as ina deep learning fashion, to provide an anticipated outcome or stateand/or to perform actions to optimize specific machines, devices,components, processes, and the like.

In embodiments, feedback provided to the expert system may be used inone or more smart bands to predict progress towards one or more goals.The expert system may use the feedback to determine a modification,alteration, addition, change, or the like to one or more components ofthe system that provided the feedback, as described elsewhere herein.Based on the industry-specific feedback, the expert system may alter aninput, a way of treating or storing an input or output, a sensor orsensors used to provide feedback, an operating parameter, a piece ofequipment used in the system, or any other aspect of the participants inthe industrial system that gave rise to the feedback. As describedelsewhere herein, the expert system may track multiple goals, such aswith one or more smart bands. Industry-specific feedback may be used inor by the smart bands in predicting an outcome or state relating to theone or more goals, and to recommend or instruct a change that isdirected in increasing a likelihood of achieving the outcome or state.

In an embodiment, a system 11100 for data collection in an industrialenvironment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors11102, and a machine learning data analysis circuit 11110 structured toreceive the output data 11108 and learn received output data patterns11112 indicative of an outcome, wherein the machine learning dataanalysis circuit 11110 is structured to learn received output datapatterns 11112 by being seeded with a model 11114 based onindustry-specific feedback 11118. The model 11114 may be a physicalmodel, an operational model, or a system model. The industry-specificfeedback 11118 may be one or more of a utilization measure, anefficiency measure (e.g., power and/or financial), a measure of successin prediction or anticipation of states (e.g., an avoidance andmitigation of faults), a productivity measure (e.g., a workflow), ayield measure, and a profit measure. The industry-specific feedback11118 includes an amount of power generated by a machine about which theinput sensors provide information during operation of the machine. Theindustry-specific feedback 11118 includes a measure of the output of anassembly line about which the input sensors provide information. Theindustry-specific feedback 11118 includes a failure rate of units ofproduct produced by a machine about which the input sensors provideinformation. The industry-specific feedback 11118 includes a fault rateof a machine about which the input sensors provide information. Theindustry-specific feedback 11118 includes the power utilizationefficiency of a machine about which the input sensors provideinformation, wherein the machine is one of a turbine, a transformer, agenerator, a compressor, one that stores energy, and one that includespower train components (e.g., the rate of extraction of a material by amachine about which the input sensors provide information, the rate ofproduction of a gas by a machine about which the input sensors provideinformation, the rate of production of a hydrocarbon product by amachine about which the input sensors provide information), and the rateof production of a chemical product by a machine about which the inputsensors provide information. The machine learning data analysis circuit11110 may be further structured to learn received output data patterns11112 based on the outcome. The system 11100 may keep or modifyoperational parameters or equipment. The controller 11106 may adjust theweighting of the machine learning data analysis circuit 11110 based onthe learned received output data patterns 11112 or the outcome, collectmore/fewer data points from the input sensors based on the learnedreceived output data patterns 11112 or the outcome, change a datastorage technique for the output data 11108 based on the learnedreceived output data patterns 11112 or the outcome, change a datapresentation mode or manner based on the learned received output datapatterns 11112 or the outcome, and apply one or more filters (low pass,high pass, band pass, etc.) to the output data 11108. In embodiments,the system 11100 may remove/re-task under-utilized equipment based onone or more of the learned received output data patterns 11112 and theoutcome. The machine learning data analysis circuit 11110 may include aneural network expert system. The input sensors may measure vibrationand noise data. The machine learning data analysis circuit 11110 may bestructured to learn received output data patterns 11112 indicative ofprogress/alignment with one or more goals/guidelines (e.g., which may bedetermined by a different subset of the input sensors). The machinelearning data analysis circuit 11110 may be structured to learn receivedoutput data patterns 11112 indicative of an unknown variable. Themachine learning data analysis circuit 11110 may be structured to learnreceived output data patterns 11112 indicative of a preferred inputamong available inputs. The machine learning data analysis circuit 11110may be structured to learn received output data patterns 11112indicative of a preferred input data collection band among availableinput data collection bands. The machine learning data analysis circuit11110 may be disposed in part on a machine, on one or more datacollectors, in network infrastructure, in the cloud, or any combinationthereof. The system 11100 may be deployed on the data collection circuit11104. The system 11100 may be distributed between the data collectioncircuit 11104 and a remote infrastructure. The data collection circuit11104 may include a data collector.

In embodiments, a system 11100 for data collection in an industrialenvironment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors,and a machine learning data analysis circuit 11110 structured to receivethe output data 11108 and learn received output data patterns 11112indicative of an outcome, wherein the machine learning data analysiscircuit 11110 is structured to learn received output data patterns 11112by being seeded with a model 11114 based on a utilization measure.

In embodiments, a system 11100 for data collection in an industrialenvironment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors,and a machine learning data analysis circuit 11110 structured to receivethe output data 11108 and learn received output data patterns 11112indicative of an outcome, wherein the machine learning data analysiscircuit 11110 is structured to learn received output data patterns 11112by being seeded with a model 11114 based on an efficiency measure.

In embodiments, a system 11100 for data collection in an industrialenvironment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors,and a machine learning data analysis circuit 11110 structured to receivethe output data 11108 and learn received output data patterns 11112indicative of an outcome, wherein the machine learning data analysiscircuit 11110 is structured to learn received output data patterns 11112by being seeded with a model 11114 based on a measure of success inprediction or anticipation of states.

In embodiments, a system 11100 for data collection in an industrialenvironment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors,and a machine learning data analysis circuit 11110 structured to receivethe output data 11108 and learn received output data patterns 11112indicative of an outcome, wherein the machine learning data analysiscircuit 11110 is structured to learn received output data patterns 11112by being seeded with a model 11114 based on a productivity measure.

Clause 1. In embodiments, a system for data collection in an industrialenvironment, comprising: a plurality of input sensors communicativelycoupled to a controller; a data collection circuit structured to collectoutput data from the input sensors; and a machine learning data analysiscircuit structured to receive the output data and learn received outputdata patterns indicative of an outcome, wherein the machine learningdata analysis circuit is structured to learn received output datapatterns by being seeded with a model based on industry-specificfeedback. 2. The system of clause 1, wherein the model is a physicalmodel, an operational model, or a system model. 3. The system of clause1, wherein the industry-specific feedback is a utilization measure. 4.The system of clause 1, wherein the industry-specific feedback is anefficiency measure. 5. The system of clause 4, wherein the efficiencymeasure is one of power and financial. 6. The system of clause 1,wherein the industry-specific feedback is a measure of success inprediction or anticipation of states. 7. The system of clause 6, whereinthe measure of success is an avoidance and mitigation of faults. 8. Thesystem of clause 1, wherein the industry-specific feedback is aproductivity measure. 9. The system of clause 8, wherein theproductivity measure is a workflow. 10. The system of clause 1, whereinthe industry-specific feedback is a yield measure. 11. The system ofclause 1, wherein the industry-specific feedback is a profit measure.12. The system of clause 1, wherein the machine learning data analysiscircuit is further structured to learn received output data patternsbased on the outcome. 13. The system of clause 1, wherein the systemkeeps or modifies operational parameters or equipment. 14. The system ofclause 1, wherein the controller adjusts the weighting of the machinelearning data analysis circuit based on the learned received output datapatterns or the outcome. 15. The system of clause 1, wherein thecontroller collects more/fewer data points from the input sensors basedon the learned received output data patterns or the outcome. 16. Thesystem of clause 1, wherein the controller changes a data storagetechnique for the output data based on the learned received output datapatterns or the outcome. 17. The system of clause 1, wherein thecontroller changes a data presentation mode or manner based on thelearned received output data patterns or the outcome. 18. The system ofclause 1, wherein the controller applies one or more filters (low pass,high pass, band pass, etc.) to the output data. 19. The system of clause1, wherein the system removes/re-tasks under-utilized equipment based onone or more of the learned received output data patterns and theoutcome. 20. The system of clause 1, wherein the machine learning dataanalysis circuit comprises a neural network expert system. 21. Thesystem of clause 1, wherein the input sensors measure vibration andnoise data. 22. The system of clause 1, wherein the machine learningdata analysis circuit is structured to learn received output datapatterns indicative of progress/alignment with one or moregoals/guidelines. 23. The system of clause 22, whereinprogress/alignment of each goal/guideline is determined by a differentsubset of the input sensors. 24. The system of clause 1, wherein themachine learning data analysis circuit is structured to learn receivedoutput data patterns indicative of an unknown variable. 25. The systemof clause 1, wherein the machine learning data analysis circuit isstructured to learn received output data patterns indicative of apreferred input among available inputs. 26. The system of clause 1,wherein the machine learning data analysis circuit is structured tolearn received output data patterns indicative of a preferred input datacollection band among available input data collection bands. 27. Thesystem of clause 1, wherein the machine learning data analysis circuitis disposed in part on a machine, on one or more data collectors, innetwork infrastructure, in the cloud, or any combination thereof 28. Thesystem of clause 1, wherein the system is deployed on the datacollection circuit. 29. The system of clause 1, wherein the system isdistributed between the data collection circuit and a remoteinfrastructure. 30. The system of clause 1, wherein theindustry-specific feedback includes an amount of power generated by amachine about which the input sensors provide information duringoperation of the machine. 31. The system of clause 1, wherein theindustry-specific feedback includes a measure of the output of anassembly line about which the input sensors provide information. 32. Thesystem of clause 1, wherein the industry-specific feedback includes afailure rate of units of product produced by a machine about which theinput sensors provide information. 33. The system of clause 1, whereinthe industry-specific feedback includes a fault rate of a machine aboutwhich the input sensors provide information. 34. The system of clause 1,wherein the industry-specific feedback includes the power utilizationefficiency of a machine about which the input sensors provideinformation. 35. The system of clause 34, wherein the machine is aturbine. 36. The system of clause 34, wherein the machine is atransformer. 37. The system of clause 34, wherein the machine is agenerator. 38. The system of clause 34, wherein the machine is acompressor. 39. The system of clause 34, wherein the machine storesenergy. 40. The system of clause 1, wherein the machine includes powertrain components. 41. The system of clause 34, wherein theindustry-specific feedback includes the rate of extraction of a materialby a machine about which the input sensors provide information. 42. Thesystem of clause 34, wherein the industry-specific feedback includes therate of production of a gas by a machine about which the input sensorsprovide information. 43. The system of clause 34, wherein theindustry-specific feedback includes the rate of production of ahydrocarbon product by a machine about which the input sensors provideinformation. 44. The system of clause 34, wherein the industry-specificfeedback includes the rate of production of a chemical product by amachine about which the input sensors provide information. 45. Thesystem of clause 1, wherein the data collection circuit comprises a datacollector. 46. A system for data collection in an industrialenvironment, comprising: a plurality of input sensors communicativelycoupled to a controller; a data collection circuit structured to collectoutput data from the input sensors; and a machine learning data analysiscircuit structured to receive the output data and learn received outputdata patterns indicative of an outcome, wherein the machine learningdata analysis circuit is structured to learn received output datapatterns by being seeded with a model based on a utilization measure.47. A system for data collection in an industrial environment,comprising: a plurality of input sensors communicatively coupled to acontroller; a data collection circuit structured to collect output datafrom the input sensors; and a machine learning data analysis circuitstructured to receive the output data and learn received output datapatterns indicative of an outcome, wherein the machine learning dataanalysis circuit is structured to learn received output data patterns bybeing seeded with a model based on an efficiency measure. 48. A systemfor data collection in an industrial environment, comprising: aplurality of input sensors communicatively coupled to a controller; adata collection circuit structured to collect output data from the inputsensors; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternsindicative of an outcome, wherein the machine learning data analysiscircuit is structured to learn received output data patterns by beingseeded with a model based on a measure of success in prediction oranticipation of states. 49. A system for data collection in anindustrial environment, comprising: a plurality of input sensorscommunicatively coupled to a controller; a data collection circuitstructured to collect output data from the input sensors; and a machinelearning data analysis circuit structured to receive the output data andlearn received output data patterns indicative of an outcome, whereinthe machine learning data analysis circuit is structured to learnreceived output data patterns by being seeded with a model based on aproductivity measure.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, set a parameter of a data collection band for collection by adata collector. The parameter may relate to at least one of setting afrequency range for collection and setting an extent of granularity forcollection.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, identify a set of sensors among a larger set of availablesensors for collection by a data collector. The user interface mayinclude views of available data collectors, their capabilities, one ormore corresponding smart bands, and the like.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, select a set of inputs to be multiplexed among a set ofavailable inputs.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, select a component of an industrial machine displayed in thegraphical user interface for data collection, view a set of sensors thatare available to provide data about the industrial machine, and select asubset of sensors for data collection.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, view a set of indicators of fault conditions of one or moreindustrial machines, where the fault conditions are identified byapplication of an expert system to data collected from a set of datacollectors. In embodiments, the fault conditions may be identified bymanufacturers of portions of the one or more industrial machines. Thefault conditions may be identified by analysis of industry trade data,third-party testing agency data, industry standards, and the like. Inembodiments, a set of indicators of fault conditions of one or moreindustrial machines may include indicators of stress, vibration, heat,wear, ultrasonic signature, operational deflection shape, and the like,optionally including any of the widely varying conditions that can besensed by the types of sensors described throughout this disclosure andthe documents incorporated herein by reference.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that enablesa user to select from a list of component parts of an industrial machinefor establishing smart-band monitoring and in response thereto presentsthe user with at least one smart-band definition of an acceptable rangeof values for at least one sensor of the industrial machine and a listof correlated sensors from which data will be gathered and analyzed whenan out of acceptable range condition is detected from the at least onesensor.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that enablesa user to select from a list of conditions of an industrial machine forestablishing smart-band monitoring and, in response thereto, presentsthe user with at least one smart-band definition of an acceptable rangeof values for at least one sensor of the industrial machine and a listof correlated sensors from which data will be gathered and analyzed whenan out of acceptable range condition is detected from the at least onesensor.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that enablesa user to select from a list of reliability measures of an industrialmachine for establishing smart-band monitoring and, in response thereto,presents the user with at least one smart-band definition of anacceptable range of values for at least one sensor of the industrialmachine and a list of correlated sensors from which data will begathered and analyzed when an out of acceptable range condition isdetected from the at least one sensor. In the system, the reliabilitymeasures may include one or more of industry average data,manufacturer's specifications, material specifications, recommendations,and the like. In embodiments, reliability measures may include measuresthat correlate to failures, such as stress, vibration, heat, wear,ultrasonic signature, operational deflection shape effect, and the like.In embodiments, manufacturer's specifications may include cycle count,working time, maintenance recommendations, maintenance schedules,operational limits, material limits, warranty terms, and the like. Inembodiments, the sensors in the industrial environment may be correlatedto manufacturer's specifications by associating a condition being sensedby the sensor to a specification type. In embodiments, a non-limitingexample of correlating a sensor to a manufacturer's specification mayinclude a duty cycle specification being correlated to a sensor thatdetects revolutions of a moving part. In embodiments, a temperaturespecification may correlate to a thermal sensor disposed to sense anambient temperature proximal to the industrial machine.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface thatautomatically creates a smart-band group of sensors disposed in theindustrial environment in response to receiving a condition of theindustrial environment for monitoring and an acceptable range of valuesfor the condition.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that presentsa representation of components of an industrial machine deployable inthe industrial environment on an electronic display, and in response toa user selecting one or more of the components, searches a database ofindustrial machine failure modes for modes involving the selectedcomponent(s) and conditions associated with the failure mode(s) to bemonitored, and further identifies a plurality of sensors in, on, oravailable to be disposed on the presented machine representation fromwhich data will automatically be captured when the condition(s) to bemonitored are detected to be outside of an acceptable range. Inembodiments, the identified plurality of sensors includes at least onesensor through which the condition(s) will be monitored.

In embodiments, a system for data collection in an industrialenvironment may include a user interface for working with smart bandsthat may facilitate a user identifying sensors to include in a smartband group of sensors by selecting sensors presented on a map of amachine in the environment. A user may be guided to select among asubset of all possible sensors based on smart band criteria, such astypes of sensor data required for the smart band. A smart band may befocused on detecting trending conditions in a portion of the industrialenvironment; therefore, the user interface may direct the user chooseamong an identified subset of sensors, such as by only allowing sensorsproximal to the smart band directed portion of the environment to beselectable in the user interface.

In embodiments, a smart band data collection configuration anddeployment user interface may include views of components in anindustrial environment and related available sensors. In embodiments, inresponse to selection of a component part of an industrial machinedepicted in the user interface, sensors associated with smart band datacollection for the component part may be highlighted so that the usermay select one or more of the sensors. User selection in this contextmay comprise a verification of an automatic selection of sensors, ormanually identifying sensors to include in the smart band sensor group.

In embodiments, in response to selection of a smart band condition, suchas trending of bearing temperature, a user interface for smart bandconfiguration and use may automatically identify and present sensorsthat contribute to smart band analysis for the condition. A user mayresponsive to this presentation of sensors, confirm or otherwiseacknowledge one or more sensors individually or as a set to be includedin the smart band data collection group.

In embodiments, a smart band user interface may present locations ofindustrial machines in an industrial environment on a map. The locationsmay be annotated with indicators of smart band data collection templatesthat are configured for collecting smart band data for the machines atthe annotated locations. The locations may be color coded to reflect adegree of smart band coverage for a machine at the location. Inembodiments, a location of a machine with a high degree of smart bandcoverage may be colored green, whereas a location of a machine with lowsmart band coverage may be colored red or some other contrasting color.Other annotations, such as visual annotations may be used. A user mayselect a machine at a location and by dragging the selected machine to alocation of a second machine, effectively configure smart bands for thesecond machine that correspond to smart bands for the first machine. Inthis way, a user may configure several smart band data collectiontemplates for a newly added machine or a new industrial environment andthe like.

In embodiments, various configurations and selections of smart bands maybe stored for use throughout a data collection platform, such as forselecting templates for sensing, templates for routing, provisioning ofdevices and the like, as well as for direct the placement of sensors,such as by personnel or by machines, such as autonomous orremote-control drones.

In embodiments, a smart band user interface may present a map of anindustrial environment that may include industrial machines,machine-specific data collectors, mobile data collectors (robotic andhuman), and the like. A user may view a list of smart band datacollection actions to be performed and may select a data collectionresource set to undertake the collection. In an example, a guided mobilerobot may be equipped with data collection systems for collecting datafor a plurality of smart band data sets. A user may view an industrialenvironment with which the robot is associated and assign the robot toperform a smart band data collection activity by selecting the robot, asmart band data collection template, and a location in the industrialenvironment, such as a machine or a part of a machine. The userinterface may provide a status of the collection undertaking so that theuser can be informed when the data collection is complete.

In embodiments, a smart band operation management user interface mayinclude presentation of smart band data collection activity, analysis ofresults, actions taken based on results, suggestions for changes tosmart band data collection (e.g., addition of sensors to a smart bandcollection template, increasing duration of data collection for atemplate-specific collection activity), and the like. The user interfacemay facilitate “what if” type analysis by presenting potential impactson reliability, costs, resource utilization, data collection tradeoffs,maintenance schedule impacts, risk of failure (increase/decrease), andthe like in response to a user's attempt to make a change to a smartband data collection template, such as a user relaxing a threshold forperforming smart band data collection and the like. In embodiments, auser may select or enter a target budget for preventive maintenance perunit time (e.g., per month, quarter, and the like) into the userinterface and an expert system of the user interface may recommend asmart band data collection template and thresholds for complying withthe budget.

In embodiments, a smart band user interface may facilitate a userconfiguring a system for data collection in an industrial environmentfor smart band data gathering. The user interface may include display ofindustrial machine components, such as motors, linkages, bearings, andthe like that a user may select. In response to such a selection, anexpert system may work with the user interface to present a list ofpotential failure conditions related to the part to monitor. The usermay select one or more conditions to monitor. The user interface maypresent the conditions to monitor as a set that the user may be asked toapprove. The user may indicate acceptance of the set or of selectconditions in the set monitor. As a follow-on to a userselection/approval of one or more conditions to monitor, the userinterface may display a map of relevant sensors available in theindustrial environment for collecting data as a smart band group ofsensors. The relevant sensors may be associated with one or more parts(e.g., the part(s) originally selected by the user), one or more failureconditions, and the like.

In embodiments, the expert system may compare the relevant sensors inthe environment to a preferred set of sensors for smart band monitoringof the failure condition(s) and provide feedback to the user, such as aconfidence factor for performing smart band monitoring based on theavailable sensors for the failure condition(s). The user may evaluatethe failure condition and smart band analysis information presented andmay take an action in the user interface, such as approving the relevantsensors. In response, a smart band data collection template forconfiguring the data collection system may be created. In embodiments, asmart band data collection template may be created independently of auser approval. In such embodiments, the user may indicate explicitly orimplicitly via approval of the smart band analysis information anapproval of the created template.

In embodiments, a smart band user interface may work with an expertsystem to present candidate portions of an industrial machine in anindustrial environment for smart band condition monitoring based oninformation such as manufacturer's specifications, statisticalinformation derived from real-world experience with similar industrialmachines, and the like. In embodiments, the user interface may permit auser to select certain aspects of the smart band data collection andanalysis process including—for example, a degree of reliability/failurerisk to monitor (e.g., near failure, best performance, industry average,and the like). In response thereto, the expert system may adjust anaspect of the smart band analysis, such as a range of acceptable valueto monitor, a monitor frequency, a data collection frequency, a datacollection amount, a priority for the data collection activity (e.g.,effectively a priority of a template for data collection for the smartband), weightings of data from sensors (e.g., specific sensors in thegroup, types of sensors, and the like).

In embodiments, a smart bands user interface may be structured to allowa user to let an expert system recommend one or more smart bands toimplement based on a range of comparative data that the user mightprioritize, such as industry average data, industry best data, near-bycomparable machines, most similarly configured machines, and the like.Based on the comparative data weighting, the expert system may use theuser interface to recommend one or more smart band templates that alignwith the weighting to the user, who may take an action in the userinterface, such as approving one or more of the recommended templatesfor use.

In embodiments, a user interface for configuring arrangement of sensorsin an industrial environment may include recommendations by industrialenvironment equipment suppliers (e.g., manufacturers, wholesalers,distributors, dealers, third-party consultants, and the like) ofgroup(s) of sensors to include for performing smart band analysis ofcomponents of the industrial equipment. The information may be presentedto a user as data collection template(s) that the user may indicate asbeing accepted/approved, such as by positioning a graphic representing atemplate(s) over a portion of the industrial equipment.

In embodiments, a smart band discovery portal may facilitate sharing ofsmart band related information, such as recommendations, actual usecases, results of smart band data collection and processing, and thelike. The discovery portal may be embodied as a panel in a smart banduser interface.

In embodiments, a smart band assessment portal may facilitate assessmentof smart band-based data collection and analysis. Content that may bepresented in such a portal may include depictions of uses of existingsmart band templates for one or more industrial machines, industrialenvironments, industries, and the like. A value of a smart band may beascribed to each smart band in the portal based, for example, onhistorical use and outcomes. A smart band assessment portal may alsoinclude visualization of candidate sensors to include in a smart banddata collection template based on a range of factors including ascribedvalue, preventive maintenance costs, failure condition being monitored,and the like.

In embodiments, a smart bands graphical user interface associated with asystem for data collection in an industrial environment may be deployedfor industrial components, such as of factory-based air conditioningunits. A user interface of a system for data collection for smart bandanalysis of air conditioning units may facilitate graphicalconfiguration of smart band data collection templates and the like forspecific air conditioning system installations. In embodiments, majorcomponents of an air conditioning system, such as a compressor,condenser, heat exchanger, ducting, coolant regulators, filters, fans,and the like along with corresponding sensors for a particularinstallation of the air conditioning system may be depicted in a userinterface. A user may select one or more of these components in the userinterface for configuring a system for smart band data collection. Inresponse to the user selecting, for example, a coolant compressor,sensors associated with the compressor may be automatically identifiedin the user interface. The user may be presented with a recommended datacollection template to perform smart band data collection for theselected compressor. Alternatively, the user may request a candidatecollection template from a community of smart band users, such asthrough a smart band template sharing panel of the user interface. Oncea template is selected, the user interface may offer the usercustomization options, such as frequency of collection, degree ofreliability to monitor, and the like. Upon final acceptance of thetemplate, the user interface may interact with a data collection systemof the installed air conditioning system (if such a system is available)to implement the data collection template and provide an indication tothe user of the result of implementing the template. In responsethereto, the user may make a final approval of the template for use withthe air conditioning unit.

Referring to FIG. 119, an exemplary user interface for smart bandconfiguration of a system for data collection in an industrialenvironment is depicted. The user interface 11200 may include anindustrial environment visualization portion 11202 in which may bedepicted one or more sensors, machines, and the like. Each sensor,machine, or portion thereof (e.g., motor, compressor, and the like) maybe selectable as part of a smart-band configuration process. Likewise,each sensor, machine or portion thereof may be visually highlightedduring the smart-band configuration process, such as in response to userselection, or automated identification as being part of a group of smartband sensors. The user interface may also include a smart band selectionportion 11204 or panel in which smart band indicators, failure modes,and the like may be depicted in selectable elements. User selection of asymptom, failure mode and the like may cause corresponding components,sensors, machines, and the like in the industrial visualization portionto be highlighted. The user interface may also include a customizationpanel 11206 in which attributes of a selected smart band, such asacceptable ranges, frequency of monitoring and the like may be madeavailable for a user to adjust.

Cause 1. In embodiments, a system comprising: a user interfacecomprising: a selectable graphical element that facilitates selection ofa representation of a component of an industrial machine from anindustrial environment in which a plurality of sensors is deployed fromwhich a data collection system collects data for the system for whichthe user interface enables interaction; and selectable graphicalelements representing a portion of the plurality of sensors thatfacilitate selection of a sensors to form a data collection subset ofsensors in the industrial environment. 2. The system of clause 1,wherein selection of sensors to form a data collection subset results ina data collection template adapted to facilitate configuring the datarouting and collection system for collecting data from the datacollection subset of sensors. 3. The system of clause 1, wherein theuser interface comprises an expert system that analyzes a user selectionof a graphical element that facilitates selection of a component andadjusts the selectable graphical elements representing a portion of theplurality of sensors to activate only sensors associated with acomponent associated with the selected graphical element. 4. The systemof clause 1, wherein the selectable graphical element that facilitatesselection of a component of an industrial machine further facilitatespresentation of a plurality of data collection templates associated withthe component. 5. The system of clause 1, wherein the portion of theplurality of sensors comprises a smart band group of sensors. 6. Thesystem of clause 5, wherein the smart band group of sensors comprisessensors for a component of the industrial machine selected by theselectable graphical element. 7. A system comprising: an expertgraphical user interface comprising representations of a plurality ofcomponents of an industrial machine from an industrial environment inwhich a plurality of sensors is deployed from which a data collectionsystem collects data for the system for which the user interface enablesinteraction, wherein at least one representation of the plurality ofcomponents is selectable by a user in the user interface; a database ofindustrial machine failure modes; and a database searching facility thatsearches the database of failure modes for modes that correspond to auser selection of a component of the plurality of components. 8. Thesystem of clause 7, comprising a database of conditions associated withthe failure modes. 9. The system of clause 8, wherein the database ofconditions includes a list of sensors in the industrial environmentassociated with the condition. 10. The system of clause 9, wherein thedatabase searching facility further searches the database of conditionsfor sensors that correspond to at least one condition and indicates thesensors in the graphical user interface. 11. The system of clause 7,wherein the user selection of a component of the plurality of componentscauses a data collection template for configuring the data routing andcollection system to automatically collect data from sensors associatedwith the selected component. 12. A method comprising: presenting in anexpert graphical user interface a list of reliability measures of anindustrial machine; facilitating user selection of one reliabilitymeasure from the list; presenting a representation of a smart band datacollection template associated with the selected reliability measure;and in response to a user indication of acceptance of the smart banddata collection template, configuring a data routing and collectionsystem to collect data from a plurality of sensors in an industrialenvironment in response to a data value from one of the plurality ofsensors being detected outside of an acceptable range of data values.13. The method of clause 12, wherein the reliability measures includeone or more of industry average data, manufacturer's specifications,manufacturer's material specifications, and manufacturer'srecommendations. 14. The method of clause 13, wherein include themanufacturer's specifications include at least one of cycle count,working time, maintenance recommendations, maintenance schedules,operational limits, material limits, and warranty terms. 15. The methodof clause 12, wherein the reliability measures correlate to failuresselected from the list consisting of stress, vibration, heat, wear,ultrasonic signature, and operational deflection shape effect. 16. Themethod of clause 12, further comprising correlating sensors in theindustrial environment to manufacturer's specifications. 17. The methodof clause 16, wherein correlating comprises matching a duty cyclespecification to a sensor that detects revolutions of a moving part. 18.The method of clause 16, wherein correlating comprises matching atemperature specification with a thermal sensor disposed to sense anambient temperature proximal to the industrial machine. 19. The methodof clause 16, further comprising dynamically setting the acceptablerange of data values based on a result of the correlating. 20. Themethod of clause 16, further comprising automatically determining theone of the plurality of sensors for detecting the data value outside ofthe acceptable range based on a result of the correlating.

In embodiments, a system for data collection in an industrialenvironment may route data from a plurality of sensors in the industrialenvironment to wearable haptic stimulators that present the data fromthe sensors as human detectable stimuli including at least one oftactile, vibration, heat, sound, and force. In embodiments, the hapticstimulus represents an effect on the machine resulting from the senseddata. In embodiments, a bending effect may be presented as bending afinger of a haptic glove. In embodiments, a vibrating effect may bepresented as vibrating a haptic arm band. In embodiments, a heatingeffect may be presented as an increase in temperature of a haptic wristband. In embodiments, an electrical effect (e.g., over voltage, current,and others) may be presented as a change in sound of a phatic audiosystem.

In embodiments, an industrial machine operator haptic user interface maybe adapted to provide haptic stimuli to the operator that is responsiveto the operator's control of the machine, wherein the stimuli indicatean impact on the machine as a result of the operator's control andinteraction with objects in the environment as a result thereof. Inembodiments, sensed conditions of the machine that exceed an acceptablerange may be presented to the operator through the haptic userinterface. In embodiments, the sensed conditions of the machine that arewithin an acceptable range may not be presented to the operator throughthe haptic user interface. In embodiments, the sensed conditions of themachine that are within an acceptable range may presented as naturallanguage representations of confirmation of the operator control. Inembodiments, at least a portion of the haptic user interface is worn bythe operator. In embodiments, a wearable haptic user interface devicemay include force exerting devices along the outer legs of a deviceoperator's uniform. When a vehicle that the operator is controllingapproaches an obstacle along a lateral side of the vehicle, aninflatable bellows may be inflated, exerting pressure against the leg ofthe operator closest to the side of the vehicle approaching theobstacle. The bellows may continue to be inflated, thereby exertingadditional pressure on the operator's leg that is consistent with theproximity of the obstacle. The pressure may be pulsed when contact withthe obstacle is imminent. In another example, an arm band of an operatormay vibrate in coordination with vibration being experienced by aportion of the vehicle that the operator is controlling. These aremerely examples and not intended to be limiting or restrictive of theways in which a wearable haptic feedback user device may be controlledto indicate conditions that are sensed by a system for data collectionin an industrial environment.

In embodiments, a haptic user interface safety system worn by a user inan industrial environment may be adapted to indicate proximity to theuser of equipment in the environment by stimulating a portion of theuser with at least one of pressure, heat, impact, electrical stimuli andthe like, the portion of the user being stimulated may be closest to theequipment. In embodiments, at least one of the type, strength, duration,and frequency of the stimuli is indicative of a risk of injury to theuser.

In embodiments, a wearable haptic user interface device, that may beworn by a user in an industrial environment, may broadcast its locationand related information upon detection of an alert condition in theindustrial environment. The alert condition may be proximal to the userwearing the device, or not proximal but related to the user wearing thedevice. A user may be an emergency responder, so the detection of asituation requiring an emergency responded, the user's haptic device maybroadcast the user's location to facilitate rapid access to the user orby the user to the emergency location. In embodiments, an alertcondition may be determined from monitoring industrial machine sensorsmay be presented to the user as haptic stimuli, with the severity of thealert corresponding to a degree of stimuli. In embodiments, the degreeof stimuli may be based on the severity of the alert, the correspondingstimuli may continue, be repeated, or escalate, optionally includingactivating multiple stimuli concurrently, send alerts to additionalhaptic users, and the like until an acceptable response is detected,e.g., through the haptic UI. The wearable haptic user device may beadapted to communicate with other haptic user devices to facilitatedetecting the acceptable response.

In embodiments, a wearable haptic user interface for use in anindustrial environment may include gloves, rings, wrist bands, watches,arm bands, head gear, belts, necklaces, shirts (e.g., uniform shirt),foot wear, pants, ear protectors, safety glasses, vests, overalls,coveralls, and any other article of clothing or accessory that can beadapted to provide haptic stimuli.

In embodiments, wearable haptic device stimuli may be correlated to asensor in an industrial environment. Non-limiting examples include avibration of a wearable haptic device in response to vibration detectedin an industrial environment; increasing or decreasing the temperatureof a wearable haptic device in response to a detected temperature in anindustrial environment; producing sound that changes in pitchresponsively to changes in a sensed electrical signal, and the like. Inembodiments, a severity of wearable haptic device stimuli may correlateto an aspect of a sensed condition in the industrial environment.Non-limiting examples include moderate or short-term vibration for a lowdegree of sensed vibration; strong or long-term vibration stimulationfor an increase in sensed vibration; aggressive, pulsed, and/ormulti-mode stimulation for a high amount of sensed vibration. Wearablehaptic device stimuli may also include lighting (e.g., flashing, colorchanges, and the like), sound, odor, tactile output, motion of thehaptic device (e.g., inflating/deflating a balloon, extension/retractionof an articulated segment, and the like), force/impact, and the like.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from fuel handling systems in a power generationapplication to the user via haptic stimulation. Fuel handling for powergeneration may include solid fuels, such as woodchips, stumps, forestresidue, sticks, energy willow, peat, pellets, bark, straw, agrobiomass, coal, and solid recovery fuel. Handling systems may includereceiving stations that may also sample the fuel, preparation stationsthat may crush or chip wood-based fuel or shred waste-based fuel. Fuelhandling systems may include storage and conveying systems, feed and ashremoval systems and the like. Wearable haptic user interface devices maybe used with fuel handling systems by providing an operator feedback onconditions in the handling environment that the user is otherwiseisolated from. Sensors may detect operational aspects of a solid fuelfeed screw system. Conditions like screw rotational rate, weight of thefuel, type of fuel, and the like may be converted into haptic stimuli toa user while allowing the user to use his hands and provide hisattention to operate the fuel feed system.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from suspension systems of a truck and/or vehicleapplication to the user via haptic stimulation. Haptic simulation may becorrelated with conditions being sensed by the vehicle suspensionsystem. In embodiments, road roughness may be detected and convertedinto vibration-like stimuli of a wearable haptic arm band. Inembodiments, suspension forces (contraction and rebound) may beconverted into stimuli that present a scaled down version of the forcesto the user through a wearable haptic vest.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from hydroponic systems in an agricultureapplication to the user via haptic stimulation. In embodiments, sensorsin hydroponic systems, such as temperature, humidity, water level, plantsize, carbon dioxide/oxygen levels, and the like may be converted towearable device haptic stimuli. As an operator wearing haptic feedbackclothing walks through a hydroponic agriculture facility, sensorsproximal to the operator may signal to the haptic feedback clothingrelevant information, such as temperature or a measure of actualtemperature versus desired temperature that the haptic clothing mayconvert into haptic stimuli. In an example, a wrist band may include athermal stimulator that can change temperature quickly to tracktemperature data or a derivative thereof from sensors in the agricultureenvironment. As a user walks through the facility, the haptic feedbackwristband may change temperature to indicate a degree to which proximaltemperatures are complying with expected temperatures.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from robotic positioning systems in an automatedproduction line application to the user via haptic stimulation. Hapticfeedback may include receiving a positioning system indicator ofaccuracy and converting it to an audible signal when the accuracy isacceptable, and another type of stimuli when the accuracy is notacceptable.

Referring to FIG. 120, a wearable haptic user interface device forproviding haptic stimuli to a user that is responsive to data collectedin an industrial environment by a system adapted to collect data in theindustrial environment is depicted. A system for data collection 11402in an industrial environment 11400 may include a plurality of sensors.Data from those sensors may be collected and analyzed by a computingsystem. A result of the analysis may be communicated wirelessly to oneor more wearable haptic feedback stimulators 11404 worn by a userassociated with the industrial environment. The wearable haptic feedbackstimulators may interpret the result, convert it into a form of stimulibased on a haptic stimuli-to-sensed condition mapping, and produce thestimuli.

Clause 1. In embodiments, a system for data collection in an industrialenvironment, comprising: a plurality of wearable haptic stimulators thatproduce stimuli selected from the list of stimuli consisting of tactile,vibration, heat, sound, force, odor, and motion; a plurality of sensorsdeployed in the industrial environment to sense conditions in theenvironment; a processor logically disposed between the plurality ofsensors and the wearable haptic stimulators, the processor receivingdata from the sensors representative of the sensed condition,determining at least one haptic stimulation that corresponds to thereceived data, and sending at least one signal for instructing thewearable haptic stimulators to produce the at least one stimulation. 2.The system of clause 1, wherein the haptic stimulation represents aneffect on a machine in the industrial environment resulting from thecondition. 3. The system of clause 2, wherein a bending effect ispresented as bending a haptic device. 4. The system of clause 2, whereina vibrating effect is presented as vibrating a haptic device. 5. Thesystem of clause 2, wherein a heating effect is presented as an increasein temperature of a haptic device. 6. The system of clause 2, wherein anelectrical effect is presented as a change in sound produced by a hapticdevice. 7. The system of clause 2, wherein at least one of the pluralityof wearable haptic stimulators are selected from the list consisting ofa glove, ring, wrist band, wrist watch, arm band, head gear, belt,necklace, shirt, foot wear, pants, overalls, coveralls, and safetygoggles. 8. The system of clause 2, wherein the at least one signalcomprises an alert of a condition of interest in the industrialenvironment. 9. The system of clause 8, wherein the at least onestimulation produced in response to the alert signal is repeated by atleast one of the plurality of wearable haptic stimulators until anacceptable response is detected. 10. An industrial machine operatorhaptic user interface that is adapted to provide the operator hapticstimuli responsive to the operator's control of the machine based on atleast one sensed condition of the machine that indicates an impact onthe machine as a result of the operator's control and interaction withobjects in the environment as a result thereof. 11. The user interfaceof clause 10, wherein a sensed condition of the machine that exceeds anacceptable range of data values for the condition is presented to theoperator through the haptic user interface. 12. The user interface ofclause 10, wherein a sensed condition of the machine that is within anacceptable range of data values for the condition is presented asnatural language representations of confirmation of the operator controlvia an audio haptic stimulator. 13. The user interface of clause 10,wherein at least a portion of the haptic user interface is worn by theoperator. 14. The system of clause 10, wherein a vibrating sensedcondition is presented as vibrating stimulation by the haptic userinterface. 15. The system of clause 10, wherein a temperature-basedsensed condition is presented as heat stimulation by the haptic userinterface. 16. A haptic user interface safety system worn by a user inan industrial environment, wherein the interface is adapted to indicateproximity to the user of equipment in the environment by hapticstimulation via a portion of the haptic user interface that is closestto the equipment, wherein at least one of the type, strength, duration,and frequency of the stimulation is indicative of a risk of injury tothe user. 17. The haptic user interface of clause 16, wherein the hapticstimulation is selected from a list consisting of pressure, heat,impact, and electrical stimulation. 18. The haptic user interface ofclause 16 wherein the haptic user interface further comprises a wirelesstransmitter that broadcasts a location of the user. 19. The haptic userinterface of clause 18, wherein the wireless transmitter broadcasts alocation of the user in response to indicating proximity of the user tothe equipment. 20. The haptic user interface of clause 16, wherein theproximity to the user of equipment in the environment is based on sensordata provided to the haptic user interface from a system adapted tocollect data in an industrial environment, wherein the system is adaptedbased on a data collection template associated with a user safetycondition in the industrial environment.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting a graphical element indicative ofindustrial machine sensed data on an augmented reality (AR) display. Thegraphical element may be adapted to represent a position of the senseddata on a scale of acceptable values of the sensed data. The graphicalelement may be positioned proximal to a sensor detected in the field ofview being augmented that captured the sensed data in the AR display.The graphical element may be a color and the scale may be a color scaleranging from cool colors (e.g., greens, blues) to hot colors (e.g.,yellow, red) and the like. Cool colors may represent data values closerto the midpoint of the acceptable range and the hot colors representingdata values close to or outside of a maximum or minimum value of therange.

In embodiments, a system for data collection in an industrialenvironment may present, in an AR display, data being collected from aplurality of sensors in the industrial environment as one of a pluralitygraphical effects (e.g., colors in a range of colors) that correlate thedata being collected from each sensor to a scale of values within anacceptable range compared to values outside of the acceptable range. Inembodiments, the plurality of graphical effects may overlay a view ofthe industrial environment and placement of the plurality of graphicaleffects may correspond to locations in the view of the environment atwhich a sensor is located that is producing the corresponding sensordata. In embodiments, a first set of graphical effects (e.g., hotcolors) represent components for which multiple sensors indicate valuesoutside acceptable ranges.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting, in an AR display informationbeing collected by sensors in the industrial environment as a heat mapoverlaying a visualization of the environment so that regions of theenvironment with sensor data suggestive of a greater potential offailure are overlaid with a graphic effect that is different thanregions of the environment with sensor data suggestive of a lesserpotential of failure. In embodiments, the heat map is based on datacurrently being sensed. In embodiments, the heat map is based on datafrom prior failures. In embodiments, the heat map is based on changes indata from an earlier period, such as data that suggest an increasedlikelihood of machine failure. In embodiments, the heat map is based ona preventive maintenance plan and a record of preventive maintenance inthe industrial environment.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting information being collected bysensors in the industrial environment as a heat map overlaying a view ofthe environment, such as a live view as may be presented in an ARdisplay. Such a system may include presenting an overlay thatfacilitates a call to action, wherein the overlay is associated with aregion of the heat map. The overlay may comprise a visual effect of apart or subsystem of the environment on which the action is to beperformed. In embodiments, the action to be performed is maintenancerelated and may be part-specific.

In embodiments, a system for data collection in an industrialenvironment may facilitate updating, in an AR view of a portion of theenvironment, a heat map of aspects of the industrial environment basedon a change to operating instructions for at least one aspect of amachine in the industrial environment. The heat map may representcompliance with operational limits for portions of machines in theindustrial environment. In embodiments, the heat map may represent alikelihood of component failure as a result of the change to operationinstructions.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting, as a heat map in an AR view of aportion of the environment, a degree or measure of coverage of sensorsin the industrial environment for a data collection template thatidentifies select sensors in the industrial environment for a datacollection activity.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map overlaying a view, suchas a live view, of an industrial environment of failure-related data forvarious portions of the environment. The failure-related data maycomprise a difference between an actual failure rate of the variousportions and another failure rate. Another failure rate may be a rate offailure of comparable portions elsewhere in the environment, and/oraverage failure rate of comparable portions across a plurality ofenvironments, such as an industry average, manufacturer failure rateestimate, and the like.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map related to datacollected from robotic arms and hands for production line robotichandling in an augmented reality view of a portion of the environment. Aheat map related to data collected from robotic arms and hands mayrepresent data from sensors disposed in—for example, the fingers of arobotic hand Sensor may collect data, such as applied pressure whenpinching an object, resistance (e.g., responsive to a robotic touch) ofan object, multi-axis forces presented to the finger as it performs anoperation, such as holding a tool and the like, temperature of theobject, total movement of the finger from initial point of contact untila resistance threshold is met, and other hand position/use conditions.Heat maps of this data may be presented in an augmented reality view ofa robotic production environment so that a user may make a visualassessment of, for example, how the relative positioning of the roboticfingers impacts the object being handled.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map related to datacollected from linear bearings for production line robotic handling inan augmented reality view of a portion of the environment. Linearbearings, as with most bearings, may not be visible while in use.However, assessing their operation may benefit from representing datafrom sensors that capture information about the bearings while in use inan augmented reality display. In embodiments, sensors may be placed todetect forces being placed on portions of the bearings by the rotatingmember or elements that the bearings support. These forces may bepresented as heat maps that correspond to relative forces, on avisualization of the bearings in an augmented reality view of a robothandling machine that uses linear bearings.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map related to datacollected from boring machinery for mining in an augmented reality viewof a portion of the environment. Boring machinery, and in particularmulti-tip circular boring heads may experience a range of rockformations at the same time. Sensors may be placed proximal to eachboring tip that may detect forces experienced by the tips. The data maybe collected by a system adapted to collect data in an industrialenvironment and provided to an augmented reality system that may displaythe data as heat maps or the like in a view of the boring machine.

Referring to FIG. 121, an augmented reality display of heat maps basedon data collected in an industrial environment by a system adapted tocollect data in the environment is depicted. An augmented reality viewof an industrial environment 11500 may include heat maps 11502 thatdepict data received from or derived from data received from sensors11504 in the industrial environment. Sensor data may be captured andprocessed by a system adapted for data collection and analysis in anindustrial environment. The data may be converted into a form that issuitable for use in an augmented reality system for displaying heatmaps. The heat maps 11502 may be aligned in the augmented reality viewwith a sensor from which the underlying data was sourced.

Clause 1. In embodiments, an augmented reality (AR) system in whichindustrial machine sensed data is presented in a view of the industrialmachine as heat maps of data collected from sensors in the view, whereinthe heat maps are positioned proximal to a sensor capturing the senseddata that is visible in the AR display. 2. The system of clause 1,wherein the heat maps are based on a comparison of real time datacollected from sensors with an acceptable range of values for the data.3. The system of clause 1, wherein the heat maps are based on trends ofsensed data. 4. The system of clause 1, wherein the heat maps representa measure of coverage of sensors in the industrial environment inresponse to a condition of interest that is calculated from datacollected by sensors in the industrial environment. 5. The system ofclause 1, wherein the heat maps of data collected from sensors in theview is based on data collected by a system adapted to collect data inthe industrial environment by routing data from a plurality of sensorsto a plurality of data collectors via at least one of an analogcrosspoint switch, a multiplexer, and a hierarchical multiplexer. 6. Thesystem of clause 1, wherein the heat maps present different collecteddata values as different colors. 7. The system of clause 1, wherein datacollected from a plurality of sensors is combined to produce a heat map.8. A system for data collection in an industrial environment,comprising: an augmented reality display that presents data beingcollected from a plurality of sensors in the industrial environment asone of a plurality of colors, wherein the colors correlate the databeing collected from each sensor to a color scale with cool colorsmapping to values of the data within an acceptable range and hot colorsmapping to values of the data outside of the acceptable range, whereinthe plurality of colors overlay a view of the industrial environment andplacement of the plurality of colors corresponds to locations in theview of the environment at which a sensor is located that is producingthe corresponding sensor data. 9. The system of clause 8, wherein hotcolors represent components for which multiple sensors indicate valuesoutside typical ranges. 10. The system of clause 8, wherein theplurality of colors is based on a comparison of real time data collectedfrom sensors with an acceptable range of values for the data. 11. Thesystem of clause 8, wherein the plurality of colors is based on trendsof sensed data. 12. The system of clause 8, wherein the plurality ofcolors represents a measure of coverage of sensors in the industrialenvironment in response to a condition of interest that is calculatedfrom data collected by sensors in the industrial environment. 13. Amethod comprising, presenting information being collected by sensors inan industrial environment as a heat map overlaying a view of theenvironment so that regions of the environment with sensor datasuggestive of a greater potential of failure are overlaid with a heatmap that is different than regions of the environment with sensor datasuggestive of a lesser potential of failure. 14. The method of clause13, wherein the heat map is based on data currently being sensed. 15.The method of clause 13, wherein the heat map is based on data fromprior failure data. 16. The method of clause 13, wherein the heat map isbased on changes in data from an earlier period that suggest anincreased likelihood of machine failure. 17. The method of clause 13,wherein the heat map is based on a preventive maintenance plan and arecord of preventive maintenance in the industrial environment. 18. Themethod of clause 13, wherein the heat map represents an actual failurerate versus a reference failure rate. 19. The method of clause 18,wherein the reference failure rate is an industry average failure rate.20. The method of clause 18, wherein the reference failure rate is amanufacturer's failure rate estimate.

In embodiments, a system for data collection and visualization thereofin an industrial environment may include an augmented reality and/orvirtual reality (AR/VR) display in which data values output by sensorsdisposed in a field of view in the AR/VR display are displayed withvisual attributes that indicate a degree of compliance of the data to anacceptable range or values for the sensed data. In embodiments, thevisual attributes may provide near real-time portrayal of trends of thesensed data and/or of derivatives thereof. In embodiments, the visualattributes may be the actual data being captured, or the derived data,such as a trend of the data and the like.

In embodiments, a system for data collection and visualization thereofin an industrial environment may include an AR/VR display in whichtrends of data values output by sensors disposed in a field of view inthe AR/VR are displayed with visual attributes that indicate a degree ofseverity of the trend. In embodiments, other data or analysis that couldbe displayed may include: data from sensors that exceed an acceptablerange, data from sensors that are part of a smart band selected by theuser, data from sensors that are monitored for triggering a smart bandcollection action, data from sensors that sense an aspect of theenvironment that meets preventive maintenance criteria, such as a PMaction is upcoming soon, a PM action was recently performed or isoverdue for PM. Other data for such AR/VR visualization may include datafrom sensors for which an acceptable range has recently been changed,expanded, narrowed and the like. Other data for such AR/VR visualizationthat may be particularly useful for an operator of an industrial machine(digging, drilling, and the like) may include analysis of data fromsensors, such as for example impact on an operating element (torque,force, strain, and the like).

In embodiments, a system for data collection and visualization thereofin an industrial environment that may include presentation of visualattributes that represent collected data in an AR/VR environment may doso for pumps in a mining application. Mining application pumps mayprovide water and remove liquefied waste from a mining site. Pumpperformance may be monitored by sensors detecting pump motors,regulators, flow meters, and the like. Pump performance monitoring datamay be collected and presented as a set of visual attributes in anaugmented reality display. In an example, pump motor power consumption,efficiency, and the like may be displayed proximal to a pump viewedthrough an augmented reality display.

In embodiments, a system for data collection and visualization thereofin an industrial environment that may include presentation of visualattributes that represent collected data in an AR/VR environment may doso for energy storage in a power generation application. Powergeneration energy storage may be monitored with sensors that capturedata related to storage and use of stored energy. Information such asutilization of individual energy storage cells, energy storage rate(e.g., battery charging and the like), stored energy consumption rate(e.g., KWH being supplied by an energy storage system), storage cellstatus, and the like may be captured and converted into augmentedreality viewable attributes that may be presented in an augmentedreality view of an energy storage system.

In embodiments, a system for data collection and visualization thereofin an industrial environment that may include presentation of visualattributes that represent collected data in an AR/VR environment may doso for feed water systems in a power generation application. Sensors maybe disposed in an industrial environment, such as power generation forcollecting data about feed water systems. Data from those sensors may becaptured and processed by the system for data collection. Results ofthis processing may include trends of the data, such as feed watercooling rates, flow rates, pressure and the like. These trends may bepresented on an augmented reality view of a feed water system byapplying a map of sensors with physical elements visible in the view andthen retrieving data from the mapped sensors. The retrieved data (andderivatives thereof) may be presented in the augmented reality view ofthe feed water system.

Referring to FIG. 122, an augmented reality display 11600 comprisingreal time data 11602 overlaying a view of an industrial environment isdepicted. Sensors 11604 in the environment may be recognized by theaugmented reality system, such as by first detecting an industrialmachine, system, or part thereof with which the sensors are associated.Data from the sensors 11604 may be retrieved from a data repository,processed into trends, and presented in the augmented reality view 11600proximal to the sensors from which the data originates.

Clause 1 In embodiments, a system for data collection and visualizationthereof in an industrial environment in which data values output bysensors disposed in a field of view in an electronic display aredisplayed in the electronic display with visual attributes that indicatea degree of compliance of the data to an acceptable range or values forthe sensed data. 2. The system of clause 1, wherein the view in theelectronic display is a view in an augmented reality display of theindustrial environment. 3. The system of clause 1, wherein the visualattributes are indicative of a trend of the sensed data over timerelative to the acceptable range. 4. The system of clause 1, wherein thedata values are disposed in the electronic display proximal to thesensors from which the data values are output. 5. The system of clause1, wherein the visual attributes further comprise an indication of asmart band set of sensors associated with the sensor from which the datavalues are output. 6. A system for data collection and visualizationthereof in an industrial environment in which data values output byselect sensors disposed in an augmented reality view of the industrialenvironment are displayed with visual attributes that indicate a degreeof compliance of the data to an acceptable range or values for thesensed data. 7. The system of clause 6, wherein the sensors are selectedbased on a data collection template that facilitates configuring sensordata routing resources in the system. 8. The system of clause 7, whereinthe select sensors are indicated in the template as part of a group ofsmart band sensors. 9. The system of clause 7, wherein the selectsensors are sensors that are monitored for triggering a smart band datacollection action. 10. The system of clause 6, wherein the selectsensors are sensors that sense an aspect of the environment associatedwith preventive maintenance criteria. 11. The system of clause 6,wherein the visual attributes further indicate if the acceptable rangehas been expanded or narrowed within the past 72 hours. 12. A system fordata collection and visualization thereof in an industrial environmentin which trends of data values output by select sensors disposed in afield of view of the industrial environment depicted in an augmentedreality display are displayed with visual attributes that indicate adegree of severity of the trend. 13. The system of clause 12, whereinsensors are selected when data from the sensors exceed an acceptablerange of values. 14. The system of clause 14, wherein sensors areselected based on the sensors being part of a smart band group ofsensors. 15. The system of clause 12, wherein the visual attributesfurther indicate a compliance of the trend with an acceptable range ofdata values. 16. The system of clause 12, wherein the system for datacollection is adapted to route data from the select sensors to acontroller of the augmented reality display based on a data collectiontemplate that facilitates configuring routing resources of the systemfor data collection. 17. The system of clause 12, wherein the sensorsare selected in response to the sensor data being configured in a smartband data collection template as an indication for triggering a smartband data collection action. 18. The system of clause 12, wherein thesensors are selected in response to preventive maintenance criteria. 19.The system of clause 18, wherein the preventive maintenance criteria areselected from the list consisting of a preventive maintenance action isscheduled, a preventive maintenance action has been completed in thelast 72 hours, a preventive maintenance action is overdue.

Referencing FIG. 124, an example storage time definition 12536 isdepicted. The example storage time definition 12536 depicts a number ofstorage locations 12556 corresponding to a number of time values 12558.It is understood that any values such as storage types, storage media,storage access, storage protocols, storage writing values, storagesecurity, and/or storage backup values, may be included in the storagetime definition 12536. Additionally or alternatively, an example storagetime definition 12536 may include process operations, events, and/orother values in addition to or as an alternative to time values 12558.The example storage time definition 12536 depicts movement of relatedsensor data to a first storage location 12550 over a first timeinterval, to a second storage location 12552 over a second timeinternal, and to a third storage location 12554 over a third timeinterval. The storage location values 12550, 12552, 12554 are depictedas an integral selection corresponding to planned storage locations, butadditionally or alternatively the values may be continuous or discrete,but not necessarily integral values. For example, a storage locationvalue 12550 of “1” may be associated with a first storage location, anda storage location value 12550 of “2” may be associated with a secondstorage location, where a value between “1” and “2” has an understoodmeaning—such as a prioritization to move the data (e.g., a “1.1”indicates that the data should be moved from “2” to “1” with arelatively high priority compared to a “1.4”), a percentage of the datato be moved (e.g., to control network utilization, memory utilization,or the like during a transfer operation), and/or a preference for astorage location with alternative options (e.g., to allow for directingstorage location, and inclusion in a cost function such that storagelocation can be balanced with other constraints in the system).Additionally or alternatively, the storage time definition 12536 caninclude additional dimensions (e.g., changing protocols, media, securityplans, etc.) and/or can include multiple options for the storage plan(e.g., providing a weighted value between 2, 3, 4, or more storagelocations, protocols, media, etc. in a triangulated ormultiple-dimension definition space).

Referencing FIG. 125 an example data resolution description 12540 isdepicted. The example data resolution description 12540 depicts a numberof data resolution values 12562 corresponding to a number of time values12564. It is understood that any values such as storage types, storagemedia, storage access, storage protocols, storage writing values,storage security, and/or storage backup values, may be included in thedata resolution description 12540. Additionally or alternatively, anexample data resolution description 12540 may include processoperations, events, and/or other values in addition to or as analternative to time values 12558. The example data resolutiondescription 12540 depicts changes in the resolution of stored relatedsensor data resolution values 12560 over time intervals, for exampleoperating at a low resolution initially, stepping up to a higherresolution (e.g., corresponding to a process start time), to a highresolution value (e.g., during a process time where the process issignificantly improved by high resolution of the related sensor data),and to a low resolution value (e.g., after a completion of the process).The example depicts a higher resolution before the process starts thanafter the process ends as an illustrative example, but the dataresolution description 12540 may include any data resolution trajectory.The data resolution values 12560 are depicted as integral selectionscorresponding to planned data resolutions, but additionally oralternatively the values may be continuous or discrete, but notnecessarily integral values. For example, data resolution values 12560of “1” may be associated with a first data resolution (e.g., a specificsampling time, byte resolution, etc.), and a data resolution values12560 of “2” may be associated with a second data resolution, where avalue between “1” and “2” has an understood meaning—such as aprioritization to sample at the defined resolution (e.g., a “1.1”indicates the data should be taken at a sampling rate corresponding to“1” with a relatively high priority compared to a “1.3”, and/or at asampling rate 10% of the way between the rate between “1” and “2”),and/or a preference for a data resolution with alternative options(e.g., to allow for sensor or network limitations, available sensorcommunication devices such as a data controller, smart sensor, orportable device taking the data from the sensor, and/or inclusion in acost function such that data resolution can be balanced with otherconstraints in the system). Additionally or alternatively, the dataresolution description 12540 can include additional dimensions (e.g.,changing protocols, media, security plans, etc.) and/or can includemultiple options for the data resolution plan (e.g., providing aweighted value between 2, 3, 4, or more data resolution values,protocols, media, etc. in a triangulated or multiple-dimensiondefinition space).

As described herein and in Appendix B attached hereto, intelligentindustrial equipment and systems may be configured in various networks,including self-forming networks, private networks, Internet-basednetworks, and the like. One or more of the smart heating systems asdescribed in Appendix B that may incorporate hydrogen production,storage, and use may be configured as nodes in such a network. Inembodiments, a smart heating system may be configured with one or morenetwork ports, such as a wireless network port that facilitateconnection through Wi-Fi and other wired and/or wireless communicationprotocols as described. The smart heating system includes a smarthydrogen production system and a smart hydrogen storage system, and thelike described in Appendix B and may be configured individually or as anintegral system connected as one or more nodes in a network ofindustrial equipment and systems. By way of this example, a smartheating system may be disposed in an on-site industrial equipmentoperations center, such as a portable trailer equipped withcommunication capabilities and the like. Such deployed smart heatingsystem may be configured, manually, automatically, or semi-automaticallyto join a network of devices, such as industrial data collection,control, and monitoring nodes and participate in network management,communication, data collection, data monitoring, control, and the like.

In another example of a smart heating system participating in a networkof industrial equipment monitoring, control, and data collection devicesin that a plurality of the smart heating systems may be configured intoa smart heating system sub-network. In embodiments, data generated bythe sub-network of devices may be communicated over the network ofindustrial equipment using the methods and systems described herein.

In embodiments, the smart heating system may participate in a network ofindustrial equipment as described herein. By way of this example, one ormore of the smart heating systems, as depicted in FIG. 126, may beconfigured as an IoT device, such as IoT device 13500 and the likedescribed herein. In embodiments, the smart heating system 13502 maycommunicate through an access point, over a mobile ad hoc network ormechanism for connectivity described herein for devices and systemselements and/or through network elements described herein.

In embodiments, one or more smart heating systems described in AppendixB may incorporate, integrate, use, or connect with facilities,platforms, modules, and the like that may enable the smart heatingsystem to perform functions such as analytics, self-organizing storage,data collection and the like that may improve data collection, deployincreased intelligence, and the like. Various data analysis techniques,such as machine pattern recognition of data, collection, generation,storage, and communication of fusion data from analog industrialsensors, multi-sensor data collection and multiplexing, self-organizingdata pools, self-organizing swarm of industrial data collectors, andothers described herein may be embodied in, enabled by, used incombination with, and derived from data collected by one or more of thesmart heating systems.

In embodiments, a smart heating system may be configured with local datacollection capabilities for obtaining long blocks of data (i.e., longduration of data acquisition), such as from a plurality of sensors, at asingle relatively high-sampling rate as opposed to multiple sets of datataken at different sampling rates. By way of this example, the localdata collection capabilities may include planning data acquisitionroutes based on historical templates and the like. In embodiments, thelocal data collection capabilities may include managing data collectionbands, such as bands that define a specific frequency band and at leastone of a group of spectral peaks, true-peak level, crest factor and thelike.

In embodiments, one or more smart heating systems may participate as aself-organizing swarm of IoT devices that may facilitate industrial datacollection. The smart heating systems may organize with other smartheating systems, IoT devices, industrial data collectors, and the liketo organize among themselves to optimize data collection based on thecapabilities and conditions of the smart heating system and needs tosense, record, and acquire information from and around the smart heatingsystems. In embodiments, one or more smart heating systems may beconfigured with processing intelligence and capabilities that mayfacilitate coordinating with other members, devices, or the like of theswarm. In embodiments, a smart heating system member of the swarm maytrack information about what other smart heating systems in a swarm arehandling and collecting to facilitate allocating data collectionactivities, data storage, data processing and data publishing among theswarm members.

In embodiments, a plurality of smart heating systems may be configuredwith distinct burners but may share a common hydrogen production systemand/or a common hydrogen storage system. In embodiments, the pluralityof smart heating systems may coordinate data collection associated withthe common hydrogen production and/or storage systems so that datacollection is not unnecessarily duplicated by multiple smart heatingsystems. In embodiments, a smart heating system that may be consuminghydrogen may perform the hydrogen production and/or storage datacollection so that as smart heating system may prepare to consumehydrogen, they coordinate with other smart heating systems to ensurethat their consumption is tracked, even if another smart heating systemperforms the data collection, handling, and the like. In embodiments,smart heating systems in a swarm may communicate among each other todetermine which smart heating system will perform hydrogen consumptiondata collection and processing when each smart heating system preparesto stop consumption of hydrogen, such as when heating, cooking, or otheruse of the heat is nearing completion and the like. By way of thisexample when a plurality of smart heating systems is actively consuminghydrogen, data collection may be performed by a first smart heatingsystem, data analytics may be performed by a second smart heatingsystem, and data and data analytics recording or reporting may beperformed by a third smart heating system. By allocating certain datacollection, processing, storage, and reporting functions to differentsmart heating systems, certain smart heating systems with sufficientstorage, processing bandwidth, communication bandwidth, available energysupply and the like may be allocated an appropriate role. When a smartheating system is nearing an end of its heating time, cooking time, orthe like, it may signal to the swarm that it will be going into powerconservation mode soon and, therefore, it may not be allocated toperform data analysis or the like that would need to be interrupted bythe power conservation mode.

In embodiments, another benefit of using a swarm of smart heatingsystems as disclosed herein is that data storage capabilities of theswarm may be utilized to store more information than could be stored ona single smart heating system by sharing the role of storing data forthe swarm.

In embodiments, the self-organizing swarm of smart heating systemsincludes one of the systems being designated as a master swarmparticipant that may facilitate decision making regarding the allocationof resources of the individual smart heating systems in the swarm fordata collection, processing, storage, reporting and the like activities.

In embodiments, the methods and systems of self-organizing swarm ofindustrial data collectors may include a plurality of additionalfunctions, capabilities, features, operating modes, and the likedescribed herein. In embodiments, a smart heating system may beconfigured to perform any or all of these additional features,capabilities, functions, and the like without limitation.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “another,” as used herein, is defined as at least a secondor more. The terms “including” and/or “having,” as used herein, aredefined as comprising (i.e., open transition).

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions, and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions, orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationswithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, code,and/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient, and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable transitory and/or non-transitorymedia, storage media, ports (physical and virtual), communicationdevices, and interfaces capable of accessing other clients, servers,machines, and devices through a wired or a wireless medium, and thelike. The methods, programs, or codes as described herein and elsewheremay be executed by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of a program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code, and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

Various embodiments described in this document relate to communicationprotocols that improve aspects of communication between nodes on a datanetwork. These aspects include, for instance, average, worst case, orvariability in communication delay, channel utilization, and/or errorrate. These embodiments are primarily described in the context of packetswitched networks, and more particularly in the context of InternetProtocol (IP) based packet switched networks. However, it should beunderstood that at least some of the embodiments are more generallyapplicable to data communication that does not use packet switching orIP, for instance based on circuit-switched of other forms of datanetworks.

Furthermore, various embodiments are described in the context of databeing sent from a “server” to a “client.” It should be understood thatthese terms are used very broadly, roughly analogous to “data source”and “data destination”. Furthermore, in at least some applications ofthe techniques, the nodes are peers, and may alternate roles as “server”and “client” or may have both roles (i.e., as data source and datadestination) concurrently. However, for the sake of exposition, exampleswhere there is a predominant direction of data flow from a “server” nodeto a “client” node are described with the understanding that thetechniques described in these examples are applicable to many othersituations.

One example for a client-server application involves a server passingmultimedia (e.g., video and audio) data, either recorded or live, to aclient for presentation to a user. Improved aspects of communicationfrom the client to the server in such an example can reducedcommunication delay, for instance providing faster startup, reducedinstances of interrupted playback, reduced instances of bandwidthreduction, and/or increased quality by more efficient channelutilization (e.g., by avoiding use of link capacity in retransmissionsor unnecessary forward error correction). This example is useful forexposition of a number of embodiments. However, it must be recognizedthat this is merely one of many possible uses of the approacheddescribed below.

FIG. 127 shows a high-level block diagram of some components that may beinterconnected on a portion of a data network. A general example of acommunication connection or session arranged on today's Internet may berepresented as a client node 125 (e.g., a client computer) communicatingwith a server node 111 (e.g., a server computer) over one network or aninterconnection of multiple networks 151-152. For example, the clientand server nodes may communicate over the public Internet using theInternet Protocol (IP). FIG. 127 additionally shows a number of nodes161, 162 positioned on the respective networks 151, 152, and a clientproxy 123 on one of the networks 152.

Referring to FIG. 128, in an example involving conventionalcommunication techniques, a client node 125 hosts a client application222, which communicates with a TCP module 226 that implements aTransmission Control Protocol (TCP). The TCP module 226 communicateswith an IP module 228 that implements an Internet Protocol forcommunicating between nodes on the interconnection of networks. Thecommunication passes between nodes of the networks over a channel 230(i.e., an abstraction of the path comprising physical links betweenequipment interconnecting the nodes of the network). Similarly, theserver node 111 hosts a server application 212, a TCP module 216, and anIP module 218. When the server application 111 and the clientapplication 222 communicate, for example, with data being passed fromthe server application to the client application, TCP module 216 at theserver node 111 and the TCP layer 226 at the client node 125 interact toimplement the two endpoints for the Transmission Control Protocol (TCP).

Generally, data units 201 (e.g., encoding of multimedia frames or otherunits of application data) generated by the server application 212 arepassed to the TCP module 216. The TCP module assembles data payloads202, for example, concatenating multiple data units 201 and/or bydividing data units 201 into multiple data payloads 202. In thediscussion below, these payloads are referred to in some instances asthe “original” or “uncoded” “packets” or original or uncoded “payloads”,which are communicated to the client (i.e., destination) node in thenetwork. Therefore, it should be understood that the word “packet” isnot used with any connotation other than being a unit of communication.In the TCP embodiment illustrated in FIG. 128, each data payload 202 is“wrapped” in a TCP packet 204, which is passed to the IP module 218,which further wraps the TCP packet 204 in an IP packet 206 fortransmission from the server node 111 to the client node 125, over whatis considered to be a IP layer channel 230 linking the server node 111and the client node 125. Note that at lower layers, such as at a datalink layer, further wrapping, unwrapping, and/or rewrapping of the IPpacket 206 may occur, however, such aspects are not illustrated in FIG.128. Generally, each payload 202 is sent in at least one TCP packet 204and a corresponding IP packet 206, and if not successfully received bythe TCP module 226 at the client node 125, may be retransmitted again bythe TCP module 216 at the server node 111 to result in successfuldelivery. The data payloads 202 are broken down into the data units 201originally provided by the server application 212 and are then deliveredin the same order to the client application 222 as they were provided bythe server application 212.

TCP implements a variety of features, including retransmission of lostpackets, maintaining order of packets, and congestion control to avoidcongestion at nodes or links along the path through the network and toprovide fair allocation of the limited bandwidth between and within thenetworks at intermediate nodes. For example, TCP implements a “windowprotocol” in which only a limited number (or range of sequence numbers)of packets are permitted to be transmitted for which end-to-endacknowledgments have not yet been received. Some implementations of TCPadjust the size of the window, for example, starting initially with asmall window (“slow start”) to avoid causing congestion. Someimplementations of TCP also control a rate of transmission of packets,for example, according to the round-trip-time and the size of thewindow.

The description below details one or more alternatives to conventionalTCP-based communication as illustrated in FIG. 128. In general, thesealternatives improve one or more performance characteristics, forexamples, one or more of overall throughput, delay, and jitter. In someapplications, these performance characteristics are directly related toapplication level performance characteristics, such as image quality ina multimedia presentation application. Referring to FIG. 127, in anumber of examples, these alternatives are directed to improvingcommunication between a server node 111 and at least one client node125. One example of such communication is streaming media from theserver node 111 to the client nodes 125, however, it should berecognized that this is only one of many examples where the describedalternatives can be used.

It should also be understood that the network configuration illustratedin FIG. 127 is merely representative of a variety of configurations. Anumber of these configurations may have paths with disparatecharacteristics. For example, a path from the server node 111 to aclient node 125 may pass over links using different types of equipmentand with very different capacities, delays, error rates, degrees ofcongestion etc. In many instances, it is this disparity that presentschallenges to achieving end-to-end communication that achieves highrate, low delay and/or low jitter. As one example, the client node 125may be a personal communication device on a wireless cellular network,the network 152 in FIG. 127 may be a cellular carrier's private wirednetwork, and network 151 may be the public Internet. In another example,the client node 125 may be a “WiFi” node of a private wireless localarea network (WLAN), network 152 may be a private local area network(LAN), and network 151 may be the public Internet.

A number of the alternatives to conventional TCP make use of a PacketCoding (PC) approach. Furthermore, a number of these approaches make useof Packet Coding essentially at the Transport Layer. Although differentembodiments may have different features, these implementations aregenerically referred to below as Packet Coding Transmission ControlProtocol (PC-TCP). Other embodiments are also described in which thesame or similar PC approaches are used at other layers, for instance, ata data link layer (e.g., referred to as PC-DL), and therefore it shouldbe understood that in general features described in the context ofembodiments of PC-TCP may also be incorporated in PC-DL embodiments.

Before discussing particular features of PC-TCP in detail, a number ofembodiments of overall system architectures are described. The laterdescription of various embodiments of PC-TCP should be understood to beapplicable to any of these system architectures, and others.

Architectures and Applications Transport Layer Architectures KernelImplementation

Referring to FIG. 129, in one architecture, the TCP modules at theserver node 111 and the client node 125 are replaced with PC-TCP modules316 and 326, respectively. Very generally, the PC-TCP module 316 at theserver accepts data units 201 from the server application 212 and formsoriginal data payloads 202 (i.e., “uncoded packets”, formed internallyto the PC-TCP module 316 and not illustrated). Very generally, thesedata payloads 202 are transported to and/or reconstructed at the PC-TCPmodule 326 at the client node 125, where the data units 201 areextracted and delivered to the client application 222 in the same orderas provided by the server application 212. As described in substantiallymore detail below, at least some embodiments of the PC-TCP modules makeuse of Random Linear Coding (RLC) for forming packets 304 fortransmission from the source PC-TCP module to the destination PC-TCPmodule, with each packet 304 carrying a payload 302, which for at leastsome packets 304 is formed from a combination of multiple originalpayloads 202. In particular, at least some of the payloads 202 areformed as linear combinations (e.g., with randomly generatedcoefficients in a finite field) of original payloads 202 to implementForward Error Correction (FEC), or as part of a retransmission or repairapproach in which sufficient information is not provided using FEC toovercome loss of packets 304 on the channel 230. Furthermore, the PC-TCPmodules 316 and 326 together implement congestion control and/or ratecontrol to generally coexist in a “fair” manner with other transportprotocols, notably conventional TCP.

One software implementation of the PC-TCP modules 316 or 326, issoftware modules that are integrated into the operating system (e.g.,into the “kernel”, for instance, of a Unix-based operating system) inmuch the same manner that a conventional TCP module is integrated intothe operating system. Alternative software implementations are discussedbelow.

Referring to FIG. 130, in an example in which a client node 125 is asmartphone on a cellular network (e.g., on an LTE network) and a servernode 111 is accessible using IP from the client node, the approachillustrated in FIG. 129 is used with one end-to-end PC-TCP sessionlinking the client node 125 and the server node 111. The IP packets 300carrying packets 304 of the PC-TCP session traverse the channel betweenthe nodes using conventional approaches without requiring anynon-conventional handling between the nodes at the endpoints of thesession.

Alternative Software Implementations

The description above includes modules generically labeled “PC-TCP”. Inthe description below, a number of different implementations of thesemodules are presented. It should be understood that, in general, anyinstance of a PC-TCP module may be implemented using any of thedescribed or other approaches.

Referring to FIG. 131, in some embodiments, the PC-TCP module 326 (orany other instance of PC-TCP module discussed in this document) isimplemented as a PC-TCP module 526, which includes a Packet Coding (PC)module 525 that is coupled to (i.e., communicates with) a conventionUser Datagram Protocol (UDP) module 524. Essentially each PC-TCP packetdescribed above consists of a PC packet “wrapped” in a UDP packet. TheUDP module 524 then communicates via the IP modules in a conventionalmanner. In some implementations, the PC module 525 is implemented as a“user space” process, which communicates with a kernel space UDP module,while in other implementations, the PC module 525 is implement in kernelspace.

Referring to FIG. 132, in some embodiments, the PC module 625, or itsfunction, is integrated into a client application 622, which thencommunicates directly with the conventional UDP module 524. The PC-TCPmodule 626 therefore effectively spans the client application 622 andthe kernel implementation of the UDP module 524. While use of UDP tolink the PC modules at the client and at the server has certainadvantages, other protocols may be used. One advantage of UDP is thatreliable transmission through use of retransmission is not part of theUDP protocol, and therefore error handling can be carried out by the PCmodules.

Referring to FIG. 133, in some implementations, a PC-TCP module 726 isdivided into one part, referred to as a PC-TCP “stub” 727, whichexecutes in the kernel space, and another part, referred to as thePC-TCP “code” 728, which executes in the user space of the operatingsystem environment. The stub 727 and the code 728 communicate to providethe functionality of the PC-TCP module.

It should be understood that these software implementations are notexhaustive. Furthermore, as discussed further below, in someimplementations, a PC-TCP module of any of the architectures or examplesdescribed in this document may be split among multiple hosts and/ornetwork nodes, for example, using a proxy architecture.

Proxy Architectures

Conventional Proxy Node

Referring to FIG. 134, certain conventional communication architecturesmake use of proxy servers on the communication path between a clientnode 125 and a server node 111. For example, a proxy node 820 hosts aproxy server application 822. The client application 222 communicateswith the proxy server application 822, which acts as an intermediary incommunication with the server application 212 (not shown in FIG. 134).It should be understood that a variety of approaches to implementingsuch a proxy are known. In some implementations, the proxy applicationis inserted on the path without the client node necessarily being aware.In some implementations, a proxy client 812 is used at the client node,in some cases forming a software “shim” between the application layerand the transport layer of the software executing at the client node,with the proxy client 812 passing communication to the proxy serverapplication. In a number of proxy approaches, the client application 222is aware that the proxy is used, and the proxy explicitly acts as anintermediary in the communication with the server application. Aparticular example of such an approach makes use of the SOCKS protocol,in which the SOCKS proxy client application (i.e., an example of theproxy client 812) communicates with a SOCKS proxy server application(i.e., an example of the proxy server application 822). The client andserver may communicate over TCP/IP (e.g., via TCP and IP modules 826 band 828 b, which may be implemented together in one TCP module), and theSOCKS proxy server application fulfills communication requests (i.e.,with the server application) on behalf of the client application (e.g.,via TCP and IP modules 826 a and 828 a). Note that the proxy serverapplication may also perform functions other than forwardingcommunication, for example, providing a cache of data that can be usedto fulfill requests from the client application.

First Alternative Proxy Node

Referring to FIG. 135, in an alternative proxy architecture, a proxynode 920 hosts a proxy server application 922, which is similar to theproxy server application 822 of FIG. 134. The client application 222communicates with the proxy server application 922, for example asillustrated using conventional TCP/IP, and in some embodiments using aproxy client 812 (e.g., as SOCKS proxy client), executing at the clientnode 125. As illustrated in FIG. 135, the proxy server application 922communicates with a server application using a PC-TCP module 926, whichis essentially the same as the PC-TCP module 326 shown in FIG. 129 forcommunicating with the PC-TCP module 316 at the server node 111.

In some embodiments, the communication architecture of FIG. 135 and theconventional communication architecture of FIG. 128 may coexist in thecommunication between the client application and the server applicationmay use PC-TCP, conventional TCP, or concurrently use both PC-TCP andTCP. The communication approach may be based on a configuration of theclient application and/or based on dialog between the client and serverapplications in establishing communication between them.

Referring to FIG. 136, in an example of the architecture shown in FIG.135, the proxy application 922 is hosted in a gateway 1020 that links alocal area network (LAN) 1050 to the Internet. A number of conventionalclient nodes 125 a-z are on the LAN, and make use of the proxy serverapplication to communicate with one or more server applications over theInternet. Various forms of gateway 1020 may be used, for instance, arouter, firewall, modem (e.g., cable modem, DSL modem etc.). In suchexamples, the gateway 1020 may be configured to pass conventional TCP/IPcommunication between the client nodes 125 a-z and the Internet, and forcertain server applications or under certain conditions (e.g.,determined by the client, the server, or the gateway) use the proxy tomake use of PC-TCP for communication over the Internet.

It should be understood that the proxy architecture shown in FIG. 135may be equally applied to server nodes 111 that communicate with a proxynode using TCP/IP, with the proxy providing PC-TCP communication withclient nodes, either directly or via client side proxies. In such cases,the proxy server application serving the server nodes may be hosted, forinstance, in a gateway device, such as a load balancer (e.g., as mightbe used with a server “farm”) that links the servers to the Internet. Itshould also be understood that in some applications, there is a proxynode associated with the server node as well as another proxy associatedwith the client node.

Integrated Proxy

Referring to FIG. 137, in some examples, a proxy server application1123, which provides essentially the same functionality as the proxyserver application 922 of FIG. 135, is resident on the client node 1121rather than being hosted on a separate network node as illustrated inFIG. 135. In such an example, the connection between the clientapplication 222 and the proxy server application 1123 is local, with thecommunication between them not passing over a data network (althoughinternally it may be passed via the IP 1129 software “stack”). Forexample, a proxy client 812 (e.g., a SOCKS client) interacts locallywith the proxy server application 1123, or the functions of the proxyclient 812 and the proxy server application 1123 are integrated into asingle software component.

Second Alternative Proxy Node

In examples of the first alternative proxy node approach introducedabove, communication between the client node and the proxy node usesconventional techniques (e.g., TCP/IP), while communication between theproxy node and the server node (or its proxy) uses PC-TCP 1127. Such anapproach may mitigate congestion and/or packet error or loss on the linkbetween the server node and the proxy node, however, it would notgenerally mitigate issues that arise on the link between the proxy nodeand the client node. For example, the client node and the proxy node maybe linked by a wireless channel (e.g., WiFi, cellular, etc.), which mayintroduce a greater degree of errors than the link between the serverand the proxy node over a wired network.

Referring to FIG. 138, in a second proxy approach, the client node 125hosts a PC-TCP module 326, or hosts or uses any of the alternatives ofsuch a module described in this document. The client application 222makes use of the PC-TCP module 326 at the client node to communicationwith a proxy node 1220. The proxy node essentially translates betweenthe PC-TCP communication with the client node 125 and conventional(e.g., TCP) communication with the server node. The proxy node 1220includes a proxy server application 1222, which makes use of a PC-TCPmodule 1226 to communicate with the client node (i.e., forms transportlayer link with the PC-TCP module 326) at the client node, and uses aconventional TCP module 826 a to communicate with the server.

Examples of such a proxy approach are illustrated in FIGS. 139-141.Referring to FIG. 139, an example of a proxy node 1220 is integrated ina wireless access device 1320 (e.g., a WiFi access point, router, etc.).The wireless access device 1320 is coupled to the server via a wiredinterface 1351 and coupled to a wireless client node 125 via a wirelessinterface 1352 at the access device and a wireless interface 1353 at theclient node. The wireless access device 1320 includes a proxy andcommunication stack implementation 1321, which includes the modulesillustrated for the proxy 1220 in FIG. 138, and the wireless client node125 includes an application and communication stack implementation 1322,which includes the modules illustrated for the client node 125 in FIG.138. Note that the IP packets 300 passing between the access device 1320and the client node 125 are generally further “wrapped” using a datalayer protocol, for example, in data layer packets 1350. As introducedabove, in some implementations, rather than implementing the PacketCoding at the transport layer, in a modification of the approach shownin FIG. 139, the Packet Coding approaches are implemented at the datalink layer.

Referring to FIG. 140, a proxy node 1220 is integrated in a node of aprivate land network of a cellular service provider. In this example,communication between a server 111 and the proxy node 1220 useconventional techniques (e.g., TCP) over the public Internet, whilecommunication between the proxy node and the client node use PC-TCP. Itshould be understood that the proxy node 1220 can be hosted at variouspoints in the service provider's network, including without limitationat a gateway or edge device that connects the provider's private networkto the Internet (e.g. a Packet Data Network Gateway of an LTE network),and/or at an internal node of the network (e.g., a serving gateway, basestation controller, etc.). Referring to FIG. 141, a similar approach maybe used with a cable television based network. PC-TCP communication maypass between a head end device and a distribution network (e.g., afiber, coaxial, or hybrid fiber-coaxial network) to individual homes.For example, each home may have devices that include PC-TCP capabilitiesthemselves, or in some example, a proxy node (e.g., a proxy nodeintegrated in a gateway 1010 as shown in FIG. 136) terminates the PC-TCPconnections at each home. The proxy node that communicates with theserver 111 using conventional approaches, while communicating usingPC-TCP over the distribution network is hosted in a node in the serviceprovider's private network, for instance at a “head end” device 1220 bof the distribution network, or in a gateway device 1220 a that linksthe service provider's network with the public Internet.

Intermediate Proxy

Referring to FIG. 142, in another architecture, the channel between aserver node and a client node is broken in to independent tandem PC-TCPlinks. An intermediate node 1620 has two instances of a PC-TCP module1626 and 1627. One PC-TCP module 1626 terminates a PC-TCP channel andcommunicates with a corresponding PC-TCP module at the server (e.g.,hosted at the server node or at a proxy associated with the servernode). The other PC-TCP module 1627 terminates a PC-TCP channel andcommunicates with a corresponding PC-TCP module at the client (e.g.,hosted at the client node or at a proxy associated with the clientnode). The two PC-TCP modules 1626 and 1627 are coupled via a routingapplication 1622, which passes decoded data units provided by one of thePC-TCP modules (e.g., module 1626 from the server node) and to anotherPC-TCP module for transmission to the client.

Note that parameters of the two PC-TCP channels that are bridged at theintermediate node 1620 do not have to be the same. For example, thebridged channels may differ in their forward error correction code rate,block size, congestion window size, pacing rate, etc. In cases in whicha retransmission protocol is used to address packet errors or lossesthat are not correctable with forward error correction coding, thePC-TCP modules at the intermediate node request or service suchretransmission requests.

In FIG. 142, only two PC-TCP modules are shown, but it should beunderstood that the intermediate node 1620 may concurrently provide alink between different pairs of server and client nodes.

Referring to FIG. 143, an example of this architecture may involve aserver node 111 communicating with an intermediate node 1620, forexample, hosted in a gateway device 1720 of a service provider networkwith the intermediate node 1620 also communicating with the client node125 via a second PC-TCP link.

Recoding Node

Referring to FIG. 144, another architecture is similar to the one shownin FIG. 142 in that an intermediate node 1820 is on a path between aserver node 111 and a client node 125, with PC-TCP communication passingbetween it and the server node and between it and the client node.

In FIG. 142, the PC-TCP modules 1626, 1627 fully decode and encode thedata passing through the node. In the approach illustrated in FIG. 144,such complete decoding is not necessary. Rather, a recoding PC-TCPmodule 1822 receives payloads 1802 a-b from PC-TCP packets 1804 a-b, andwithout decoding to reproduce the original uncoded payloads 202 (notshown), the module uses the received PC-TCP packets to send PC-TCPpackets 304, with coded payloads 302, toward the destination. Details ofvarious recoding approaches are described further later in thisdocument. However, in general, the processing by the recoding PC-TCPmodule includes one or more of the following functions: forwardingPC-TCP packets without modification to the destination; “dropping”received PC-TCP packets without forwarding, for example, if theredundancy provided by the received packets are not needed on theoutbound link; generating and transmitting new PC-TCP packets to provideredundancy on the outbound link. Note that the recording PC-TCP modulemay also provide acknowledgement information on the inbound PC-TCP link(e.g., without requiring acknowledgement from the destination node), forexample, to the server, and process received acknowledgements on theoutbound link. The processing of the received acknowledgements mayinclude causing transmission of additional redundant information in thecase that the originally provided redundancy information was notsufficient for reconstruction of the payload data.

In general, the recoding PC-TCP module maintains separate communicationcharacteristics on the inbound and outbound PC-TCP channels. Therefore,although it does not decode the payload data, it does provide controland, in general, the PC-TCP channels may differ in their forward errorcorrection code rate, block size, congestion window size, pacing rate,etc.

Multipath Transmission Single Endpoint Pair

In examples described above, a single path links the server node 111 andthe client node 125. The possibility of using conventional TCPconcurrently with PC-TCP between two nodes was introduced. Moregenerally, communication between a pair of PC-TCP modules (i.e., one atthe server node 111 and one at the client node 125) may follow differentpaths.

Internet protocol itself supports packets passing from one node toanother following different paths and possibly being delivered out oforder. Multiple data paths or channels can link a pair of PC-TCP modulesand be used for a single session. Beyond native multi-path capabilitiesof IP networks, PC-TCP modules may use multiple explicit paths for aparticular session. For example, without intending to be exhaustive,combinations of the following types of paths may be used:

Uncoded TCP and PC over UDPPC over conventional TCP and UDPPC-TCP over wireless LAN (e.g., WiFi, 802.11) and cellular data (e.g.,3G, LTE)

PC-TCP concurrently over multiple wireless base stations (e.g., viamultiple wireless LAN access points)

In some examples, Network Coding is used such that the multiple pathsfrom a server node to a client node pass through one or moreintermediate nodes at which the data is recoded, thereby causinginformation for different data units to effectively traverse differentpaths through the network.

One motivation for multipath connection between a pair of endpointsaddresses possible preferential treatment of TCP traffic rather than UDPtraffic. Some networks (e.g. certain public Wi-Fi, cable televisionnetworks, etc.) may limit the rate of UDP traffic, or drop UDP packetspreferentially compared to TCP (e.g., in the case of congestion). It maybe desirable to be able to detect such scenarios efficiently withoutlosing performance. In some embodiments, a PC-TCP session initiallyestablishes and divides the transmitted data across both a TCP and a UDPconnection. This allows comparison of the throughput achieved by bothconnections while sending distinct useful data on each connection. Anidentifier is included in the initial TCP and UDP handshake packets toidentify the two connections as belonging to the same coded PC-TCPsession, and non-blocking connection establishment can be employed so asto allow both connections to be opened at the outset without additionaldelay. The transmitted data is divided across the two connections usinge.g. round-robin (sending alternating packets or runs of packets on eachconnection) or load-balancing/back pressure scheduling (sending eachpacket to the connection with the shorter outgoing data queue). Suchalternation or load balancing can be employed in conjunction withtechniques for dealing with packet reordering. Pacing rate andcongestion window size can be controller separately for the UDP and theTCP connection, or can be controlled together. By controlling the twoconnections together (e.g., using only a single congestion window toregulate the sum of the number of packets in flight on both the TCP andUDP connections) may provide a greater degree of “fairness” as comparedto separate control.

In some examples, the adjustment of the fraction of messages transmittedover each data path/protocol is determined according to the relativeperformance/throughput of the data paths/protocols. In some examples,the adjustment of allocation of messages occurs only during an initialportion of the transmission. In other examples, the adjustment ofallocation of messages occurs on an ongoing basis throughout thetransmission. In some examples, the adjustment reverses direction (e.g.,when a data path stops preferentially dropping UDP messages, the numberof messages transmitted over that data path may increase).

In some embodiments the PC-TCP maintains both the UDP based traffic andthe TCP based traffic for the duration of the session. In otherembodiments, the PC-TCP module compares the behavior of the UCP and TCPtraffic, for example over a period specified in terms of time intervalor number of packets, where these quantities specifying the period canbe set as configuration parameters and/or modified based on previouscoded TCP sessions, e.g. the comparison period can be reduced oreliminated if information on relative TCP/UDP performance is availablefrom recent PC-TCP sessions. If the UDP connection achieves betterthroughput, the PC-TCP session can shift to using UDP only. If the TCPconnection achieves better throughput, the PC-TCP session can shift tousing TCP. In some embodiments, different types of traffic are sent overthe TCP link rather than the UDP link. In one such example, the UDPconnection is used to send some forward error correction for packetswhere it is beneficial to reduce retransmission delays, e.g. the lastblock of a file or intermediate blocks of a stream. In this example, theuncoded packets may be sent over a TCP stream with forward errorcorrection packets sent over UDP. If the receiver can use the forwarderror correction packets to recover from erasures in the TCP stream, amodified implementation of the TCP component of the receiver's PC-TCPmodule may be able to avoid using a TCP-based error recovery procedure.On the other hand, non-delivery of a forward error correction packetdoes not cause an erasure of the data that is to be recovered at thereceiver, and therefore unless there is an erasure both on the UDP pathand on the TCP path, dropping of a UDP packet does not cause delay.

Distributed Source

In some examples, multiple server nodes communicate with a client node.One way this can be implemented is with there being multiplecommunication sessions each involving one server node and one clientnode. In such an implementation, there is little or no interactionbetween a communication session between one server node and the clientnode and another communication session between another server node andthe client node. In some examples, each server node may have differentparts of a multimedia file, with each server providing its parts forcombination at the client node.

Distributed Content Delivery

In some examples, there is some relationship between the contentprovided by different servers to the client. One example of such arelationship is use of a distributed RAID approach in which redundancyinformation (e.g., parity information) for data units at one or moreservers is stored at and provided from another server. In this way,should a data unit not reach the client node from one of the servernodes, the redundancy information may be preemptively sent or requestedfrom the other node, and the missing data unit reconstructed.

In some examples, random linear coding is performed on data units beforethey are distributed to multiple server nodes as an alternative to useof distributed RAID. Then each server node establishes a separatecommunication session with the client node for delivery of part of thecoded information. In some of these examples, the server nodes havecontent that has already been at least partially encoded and thencached, thereby avoiding the necessity of repeating that partialencoding for different client nodes that will received the sameapplication data units. In some examples, the server nodes may implementsome of the functionality of the PC modules for execution duringcommunication sessions with client nodes, for example, having theability to encode further redundancy information in response toacknowledgment information (i.e., negative acknowledgement information)received from a client node.

In some implementations, the multiple server nodes are content deliverynodes to which content is distributed using any of a variety of knowntechniques. In other implementations, these multiple server nodes areintermediary nodes at which content from previous content deliverysessions was cached and therefore available without requiringre-delivery of the content from the ultimate server node.

In some examples of distributed content delivery, each server to clientconnection is substantially independent, for example, with independentlydetermined communication parameters (e.g., error correction parameters,congestion window size, pacing rate, etc.). In other examples, at leastsome of the parameters are related, for example, with characteristicsdetermined on one server-to-client connection being used to determinehow the client node communicates with other server nodes. For example,packet arrival rate, loss rate, and differences in one-way transmissionrate, may be measured on one connections and these parameters may beused in optimizing multipath delivery of data involving other servernodes. One manner of optimization may involve load balancing acrossmultiple server nodes or over communication links on the paths from theserver nodes to the client nodes.

In some implementations, content delivery from distributed server nodesmaking use of PC-TCP, either using independent sessions or usingcoordination between sessions, may achieve the performance ofconventional distributed content delivery but requiring a smaller numberof server nodes. This advantage may arise due to PC-TCP providing lowerlatency and/or lower loss rates than achieved with conventional TCP.

Multicast

FIGS. 145-146 show two examples of delivery of common content tomultiple destination nodes simultaneously via multicast connections. Theadvantage of multicast is that a single packet or block of N packets hasto be sent by the source node into the network and the network willattempt to deliver the packets to all destination nodes in the multicastgroup. If the content needs to be delivered reliably, then TCP will mostlikely be used as the transport layer protocol. To achieve reliability,TCP requires destination nodes to respond with acknowledgments andspecify the packets that each destination node is missing. If there are10s of thousands or 100s of thousands of receivers, and each destinationnode is missing a different packet or set of packets, the number ofdifferent retransmissions to the various receivers will undercut theadvantages of the simultaneous transmission of the content to alldestination nodes at once. With network coding and forward errorcorrection, a block of N packets can be sent to a large number ofmulticast destination nodes at the same time. The paths to thesemultiple destination nodes can be similar (all over a large WiFi orEthernet local area network) or disparate (some over WiFi, some overcellular, some over fiber links, and some over various types ofsatellite networks). The algorithms described above that embodytransmission and congestion control, forward error correction, senderbased pacing, receiver based pacing, stream based parameter tuning,detection and correction for missing and out of order packets, use ofinformation across multiple connections, fast connection start and stop,TCP/UDP fallback, cascaded coding, recoding by intermediate nodes, andcoding of the ACKs can be employed to improve the throughput andreliability of delivery to each of the multicast destination node. Whenlosses are detected and coding is used, the extra coded packets can besent to some or all destination nodes. As long as N packets are receivedat each destination node, the missing packets at each destination nodecan be reconstructed from the coded packets if the number of extra codedpackets match or exceed the number of packets lost at all of thereceivers. If fewer than N packets are received at any of thedestination nodes, any set of different coded packets from the block ofN packets can be retransmitted and used to reconstruct any missingpacket in the block at each of the destination nodes. If somedestination nodes are missing more than one packet, then the maximumnumber of coded packets to be retransmitted will be equal to the largestnumber of packets that are missing by any of the destination nodes.These few different coded packets can be used to reconstruct the missingpackets at each of the destination nodes. For example if the mostpackets missing at any destination node is four, then any four differentcoded packets can be retransmitted.

Further Illustrative Examples

FIGS. 147-157 show exemplary embodiments of data communication systemsand devices and highlight various ways to implement the novel PC-TCPdescribed herein. These configurations identify some of the possiblenetwork devices, configurations, and applications that may benefit fromusing PC-TCP, but there are many more devices, configurations andapplications that may also benefit from PC-TCP. The followingembodiments are described by way of example, not limitation.

In an exemplary embodiment depicted in FIG. 147, a user device 404 suchas a smartphone, a tablet, a computer, a television, a display, anappliance, a vehicle, a home server, a gaming console, a streaming mediabox and the like, may include a PC-TCP proxy that may interface withapplications running in the user device 404. The application on the userdevice 404 may communicate with a resource in the cloud 402 a such as aserver 408. The server 408 may be a file server, a web server, a videoserver, a content server, an application server, a collaboration server,an FTP server, a list server, a telnet server, a mail server, a proxyserver, a database server, a game server, a sound server, a printserver, an open source server, a virtual server, an edge server, astorage device and the like, and may include a PC-TCP proxy that mayinterface with applications and/or processes running on the server 408.In embodiments, the server in the cloud may terminate the PC-TCPconnection and interface with an application on the server 408 and/ormay forward the data on to another electronic device in the network. Inembodiments, the data connection may travel a path that utilizes theresources on a number of networks 402 a, 402 b. In embodiments PC-TCPmay be configured to support multipath communication such as for examplefrom a video server 408 through a peering point 406, though a carriernetwork 402 b, to a wireless router or access point 410 to a user device404 and from a video server 408 through a peering point 406, though acarrier network 402 b, to a cellular base station or cell transmitter412 to a user device 404. In embodiments, the PC-TCP may includeadjustable parameters that may be adjusted to improve multipathperformance. In some instances, the exemplary embodiment shown in FIG.147 may be referred to as an over-the-top (OTT) embodiment.

In embodiments, such as the exemplary embodiments shown in FIG. 148 andFIG. 149, other devices in the network may comprise PC-TCP proxies. Forexample, the wireless access point or router 410 and the base station orcell transmitter 412 may comprise PC-TCP proxies. In embodiments, theuser device 404 may also comprise a PC-TCP proxy (FIG. 149) or it maynot (FIG. 148). If the user device does not comprise a PC-TCP proxy, itmay communicate with the access point 410 and/or base station 412 usinga wireless or cellular protocol and/or conventional TCP or UDP protocol.The PC-TCP proxy in either or both the access point 410 and base station412 may receive data packets using these conventional communications andmay convert these communications to the PC-TCP for a connection to videoserver 408. In embodiments, if conventional TCP provides the highestspeed connection between the end user device 404 and/or the access point410 or the base station 412, then the PC-TCP proxy may utilize only someor all of the features in PC-TCP that may be compliant with and maycompliment conventional TCP implementations and transmit the data usingthe TCP layer.

FIG. 150 shows an exemplary embodiment where a user device may comprisea PC-TCP proxy and may communicate with a PC-TCP proxy server 408 on aninternet. In this embodiment, an entity may provide support for highspeed internet connections by renting, buying services from, ordeploying at least one server in the network and allowing other serversor end user devices to communicate with it using PC-TCP. The at leastone server in the network running PC-TCP may connect to other resourcesin the network and/or end users using TCP or UDP.

In embodiments, such as the exemplary embodiments shown in FIG. 151 andFIG. 152, other devices in the network may comprise PC-TCP proxies. Forexample, the wireless access point or router 410 and the base station orcell transmitter 412 may comprise PC-TCP proxies. In embodiments, theuser device 404 may also comprise a PC-TCP proxy (FIG. 152) or it maynot (FIG. 151). If the user device does not comprise a PC-TCP proxy, itmay communicate with the access point 410 and/or base station 412 usinga wireless or cellular protocol and/or conventional TCP or UDP protocol.The PC-TCP proxy in either or both the access point 410 and base station412 may receive data packets using these conventional communications andmay convert these communications to the PC-TCP for a connection toPC-TCP server 408. In embodiments, if conventional TCP provides thehighest speed connection between the end user device 404 and/or theaccess point 410 or the base station 412, then the PC-TCP proxy mayutilize only some or all of the features in PC-TCP that may be compliantwith and may compliment conventional TCP implementations and transmitthe data using the TCP layer.

In embodiments, at least some network servers 408 may comprise PC-TCPproxies and may communicate with any PC-TCP servers or devices usingPC-TCP. In other embodiments, network servers may communicate withPC-TCP servers or devices using conventional TCP and/or other transportprotocols running over UDP.

In exemplary embodiments as depicted in FIG. 153, ISPs and/or carriersmay host content on one or more servers that comprise PC-TCP proxies. Inembodiments, devices such as set-top boxes, cable boxes, digital videorecorders (DVRs), modems, televisions, smart televisions, internettelevisions, displays, and the like may comprise PC-TCP proxies. A userdevice 404 such as described above, may include a PC-TCP proxy that mayinterface with applications running in the user device 404. Theapplication on the user device 404 may communicate with a resource inthe cloud 402 c such as a server 408. The server 408 may be any type ofcommunications server as describe above, and may include a PC-TCP proxythat may interface with applications and/or processes running on theserver 408. In embodiments, the server in the cloud may terminate thePC-TCP connection and interface with an application on the server 408and/or may forward the data on to another electronic device in thenetwork. In embodiments, the data connection may travel a path thatutilizes the resources on a number of networks 402 a, 402 b, 402 c. Inembodiments PC-TCP may be configured to support multipath communicationsuch as for example from a video server 408 through a direct peeringpoint (DP) 406, to a wireless router or access point 410 or a basestation 412 to a user device 404 and from a video server 408 directly toan access point 410 and/or to a cellular base station or celltransmitter 412 to a user device 404. In embodiments, the PC-TCP mayinclude adjustable parameters that may be adjusted to improve multipathperformance.

The exemplary placements of networking devices in the communicationscenarios described above should not be taken as limitations. It shouldbe recognized that PC-TCP proxies can be placed in any network deviceand may support any type of data connection. That is, any type ofend-user device, switching device, routing device, storage device,processing device and the like, may comprise PC-TCP proxies. Also PC-TCPproxies may reside only in the end-nodes of a communication path and/oronly at two nodes along a connection path. However, PC-TCP proxies mayalso reside in more than two nodes of a communication path and maysupport multi-cast communications and multipath communications. PC-TCPproxies may be utilized in point-to-point communication networks,multi-hop networks, meshed networks, broadcast networks, storagenetworks, and the like.

Packet Coding (PC)

The description above focuses on architectures in which a packet codingapproach is deployed, and in particular architectures in which atransport layer PC-TCP approach is used. In the description below, anumber of features of PC-TCP are described. It should be understood thatin general, unless otherwise indicated, these features are compatiblewith one another and can be combined in various combinations to addressparticular applications and situations.

Data Characteristics

As introduced above, data units (e.g., audio and/or video frames) aregenerally used to form data packets, for example, with one data unit perdata packet, with multiple data units per data packet, or in someinstances separating individual data units into multiple data packets.In some applications, the data units and associated data frames form astream (e.g., a substantially continuous sequence made available overtime without necessarily having groupings or boundaries in thesequence), while in other applications, the data units and associateddata frames form one or more batches (e.g., a grouping of data that isrequired as a whole by the recipient).

In general, stream data is generated over time at a source and consumedat a destination, typically at a substantially steady rate. An exampleof a stream is a multimedia stream associated with person-to-personcommunication (e.g., a multimedia conference). Delay (also referred toas latency) and variability in delay (also referred to as jitter) areimportant characteristics of the communication of data units from asource to a destination.

An extreme example of a batch is delivery of an entire group of data,for example, a multiple gigabyte sized file. In some such examples,reducing the overall time to complete delivery (e.g., by maximizingthroughput) of the batch is of primary importance. One example of batchdelivery that may have very sensitive time (and real-time update)restraints is database replication.

In some applications, the data forms a series of batches that requiredelivery from a source to a destination. Although delay in start ofdelivery and/or completion of delivery of a batch of data units may beimportant, in many applications overall throughput may be mostimportant. An example of batch delivery includes delivery of portions ofmultimedia content, for instance, with each batch corresponding tosections of viewing time (e.g., 2 seconds of viewing time or 2 MB perbatch), with content being delivered in batches to the destination wherethe data units in the batches are buffered and used to construct acontinuous presentation of the content. As a result, an importantconsideration is the delivery of the batches in a manner than providescontinuity between batches for presentation, without “starving” thedestination application because a required batch has not arrived intime. In practice, such starving may cause “freezing” of videopresentation in multimedia, which is a phenomenon that is all toofamiliar to today's users of online multimedia delivery. Anotherimportant consideration is reduction in the initial delay in providingthe data units of the first batch to the destination application. Suchdelay is manifested, for example, in a user having to wait for initialstartup of video presentation after selecting multimedia for onlinedelivery. Another consideration in some applications is overallthroughput. This may arise, for example, if the source application hascontrol over a data rate of the data units, for example, being able toprovide a higher fidelity version of the multimedia content if higherthroughput can be achieved. Therefore, an important consideration may beproviding a sufficiently high throughput in order to enable delivery ofa high fidelity version of the content (e.g., as opposed to greatlycompressed version or a backed-off rate of the content resulting inlower fidelity).

Various packet coding approaches described below, or selection ofconfiguration parameters of those approaches, address considerationsthat are particularly relevant to the nature of the characteristics ofthe data being transported. In some examples, different approaches orparameters are set in a single system based on a runtime determinationof the nature of the characteristics of the data being transported.

Channel Characteristics

In general, the communication paths that link PC-TCP source anddestination endpoints exhibit both relatively stationary or consistentchannel characteristics, as well as transient characteristics.Relatively stationary or consistent channel characteristics can include,for example, capacity (e.g., maximum usable throughput), latency (e.g.,transit time of packets from source to destination, variability intransit time), error rate (e.g., average packet erasure or error rate,burst characteristics of erasures/errors). In general, such relativelystationary or consistent characteristics may depend on the nature of thepath, and more particularly on one or more of the links on the path. Forexample, a path with a link passing over a 4G cellular channel mayexhibit very different characteristics than a path that passes over acable television channel and/or a WiFi link in a home. As discussedfurther below, at least some of the approaches to packet coding attemptto address channel characteristic differences between types ofcommunication paths. Furthermore, at least some of the approachesinclude aspects that track relatively slow variation in characteristics,for example, adapting to changes in average throughput, latency, etc.

Communication characteristics along a path may also exhibit substantialtransient characteristics. Conventional communication techniques includeaspects that address transient characteristics resulting from congestionalong a communication path. It is well known that as congestionincreases, for example at a node along a communication path, it isimportant that traffic is reduced at that node in order to avoid anunstable situation, for instance, with high packet loss resulting frombuffer overruns, which then further increases data rates due toretransmission approaches. One common approach to addressingcongestion-based transients uses an adaptive window size of “in flight”packets that have not yet been acknowledged by their destinations. Thesize of the window is adapted at each of the sources to avoidcongestion-based instability, for example, by significantly reducing thesize of the window upon detection of increased packet erasure rates.

In addressing communication over a variety of channels, it has beenobserved that transients in communication characteristics may not be duesolely to conventional congestion effects, and that conventionalcongestion avoidance approaches may not be optimal or even desirable.Some effects that may affect communication characteristics, and that maytherefore warrant adaptation of the manner in which data is transmittedcan include one or more of the follow:

Effects resulting from cell handoff in cellular systems, includinginterruptions in delivery of packets or substantial reordering ofpackets delivered after handoff;

Effects resulting from “half-duplex” characteristics of certain wirelesschannels, for example, in WiFi channels in which return packets from adestination may be delayed until the wireless channel is acquired forupstream (i.e., portable device to access point) communication;

Effects of explicit data shaping devices, for example, intended tothrottle certain classes of communication, for instance, based on aservice provider's belief that that class of communication is maliciousor is consuming more than a fair share of resources.

Although transient effects, which may not be based solely on congestion,may be tolerated using conventional congestion avoidance techniques, oneor more of the approaches described below are particularly tailored tosuch classes of effects with the goal of maintaining efficient use of achannel without undue “over-reaction” upon detection of a transientsituation, while still avoiding causing congestion-based packet loss.

Inter-Packet Coding

In general, the coding approaches used in embodiments described in thisdocument make use of inter-packet coding in which redundancy informationis sent over the channel such that the redundancy information in onepacket is generally dependent on a set of other packets that have beenor will be sent over the channel. Typically, for a set of N packets ofinformation, a total of N+K packets are sent in a manner that erasure orany K of the packets allows reconstruction of the original N packets ofinformation. In general, a group of N information packets, or a group ofN+K packets including redundancy information (depending on context), isreferred to below as a “block” or a “coding block”. One example of sucha coding includes N information packets without further coding, and thenK redundancy packets, each of which depends on the N informationpackets. However it should be understood more than K of the packets(e.g., each of the N+K packets) may in some embodiments depend on allthe N information packets.

Forward Error Correction and Repair Retransmission

Inter-packet coding in various embodiments described in this documentuse one or both of pre-emptive transmission of redundant packets,generally referred to as forward error correction (FEC), andtransmission of redundant packets upon an indication that packets haveor have a high probability of having been erased based on feedback,which is referred to below as repair and/or retransmission. The feedbackfor repair retransmission generally comes from the receiver, but moregenerally may come from a node or other channel element on the path tothe receiver, or some network element having information about thedelivery of packets along the path. In the FEC mode, K redundant packetsmay be transmitted in order to be tolerant of up to K erasures of the Npackets, while in the repair mode, in some examples, for each packetthat the transmitter believes has been or has high probability of havingbeen erased, a redundant packet it transmitted from the transmitter,such that if in a block of N packets, K packets are believed to havebeen erased based on feedback, the transmitter sends at least anadditional K packets.

As discussed more fully below, use of a forward error correction modeversus a repair mode represents a tradeoff between use of more channelcapacity for forward error correction (i.e., reduced throughout ofinformation) versus incurring greater latency in the presence oferasures for repair retransmission. As introduced above, the datacharacteristics being transmitted may determine the relative importanceof throughput versus latency, and the PC-TCP modules may be configuredor adapted accordingly.

If on average the packet erasure rate E is less than K/(N+K), then “onaverage” the N+K packets will experience erasure of K or fewer of thepackets and the remaining packets will be sufficient to reconstruct theoriginal N. Of course even if E is not greater than K/(N+K), randomvariability, non-stationarity of the pattern of erasures etc. results insome fraction of the sets of N+K packets having greater than K erasures,so that there is insufficient information to reconstruct the N packetsat the destination. Therefore, even using FEC, at least some groups of Ninformation packets will not be reconstructable. Note, for example, withE=0.2, N=8, and K=2, even though only 2 erasures may be expected onaverage, the probability of more than 2 erasures is greater than 30%,and even with E=0.1 this probability is greater than 7%, therefore thenature (e.g., timing, triggering conditions etc.) of the retransmissionapproaches may be significant, as discussed further below. Also asdiscussed below, the size of the set of packets that are coded togetheris significant. For example, increasing N by a factor of 10 to K+N=100reduces the probably of more than the average number of 20 erasures(i.e., too many erasures to reconstruct the N=80 data packets) from over7% to less than 0.1%.

Also as discussed further below, there is a tradeoff between use oflarge blocks of packets (i.e., large N) versus smaller blocks. For aparticular code rate R=N/(N+K), longer blocks yield a higher probabilityof being able to fully recover the N information packets in the presenceof random errors. Accordingly, depending on the data characteristics,the PC-TCP modules may be configured to adapt to achieve a desiredtradeoff.

In general, in embodiments that guarantee delivery of the N packets,whether or not FEC is used, repair retransmission approaches are used toprovide further information for reconstructing the N packets. Ingeneral, in preferred embodiments, the redundancy information is formedin such a manner that upon an erasure of a packet, the redundancyinformation that is sent from the transmitter does not depend on thespecific packets that were erased, and is nevertheless suitable forrepairing the erasure independent of which packet was erased.

Random Linear Coding

In general, a preferred approach to inter-packet coding is based onRandom Linear Network Coding (RLNC) techniques. However, it should beunderstood that although based on this technology, not all features thatmay be associated with this term are necessarily incorporated. Inparticular, as described above in the absence of intermediate nodes thatperform recoding, there is not necessarily a “network” aspect to theapproach. Rather, redundancy information is generally formed bycombining the information packets into coded packets using arithmeticcombinations, and more specifically, as sums of products of coefficientsand representation of the information packets over arithmetic fields,such as finite fields (e.g., Galois Fields of order p^(n)). In general,the code coefficients are chosen from a sufficiently large finite fieldin a random or pseudo-random manner, or in another way that thecombinations of packets have a very low probability or frequency ofbeing linearly dependent. The code coefficients, or a compressed version(e.g., as a reference into a table shared by the transmitter andreceiver), are included in each transmitted combination of data units(or otherwise communicated to the receiver) and used for decoding at thereceiver. Very generally, the original information packets may berecovered at a receiver by inverting the arithmetic combinations. Forexample, a version of Gaussian Elimination may be used to reconstructthe original packets from the coded combinations. A key feature of thisapproach is that for a set of N information packets, as soon at thereceiver has at least N linearly independent combinations of thoseinformation packets in received packets, it can reconstruct the originaldata units. The term “degree of freedom” is generally used below torefer to a number of independent linear combinations, such that if Ndegrees of freedom have been specified for N original packets, then theN original packets can be reconstructed; while if fewer than N degreesof freedom are available, it may not be possible to fully reconstructany of the N original packets. If N+K linearly independent linearcombinations are sent, then any N received combinations (i.e., Nreceived degrees of freedom) are sufficient to reconstruct the originalinformation packets.

In some examples, the N+K linearly independent combinations comprise Nselections of the N “uncoded” information packets (essentially N−1 zerocoefficients and one unit coefficient for each uncoded packet), and Kcoded packets comprising the random arithmetic combination with Nnon-zero coefficients for the N information packets. The N uncodedpackets are transmitted first, so that in the absence of erasures theyshould be completely received as soon as possible. In the case of oneerasure of the original N packets, the receiver must wait for thearrival of one redundant packet (in addition to the N−1 originalpackets), and once that packet has arrived, the erased packet may bereconstructed. In the case of forward error correction, the K redundantpackets follow (e.g., immediately after) the information packets, andthe delay incurred in reconstructing the erased information packetdepends on the transmission time of packets. In the case of repairretransmission, upon detection of an erasure or high probability of anerasure, the receiver provides feedback to the transmitter, which sendsthe redundancy information upon receiving the feedback. Therefore, thedelay in being able to reconstruct the erased packet depends on theround-trip-time from the receiver to the transmitter and back.

As discussed in more detail below, feedback from the receiver to thetransmitter may be in the form of acknowledgments sent from the receiverto the transmitter. This feedback in acknowledgements at least informsthe transmitter of a number of the N+K packets of a block that have beensuccessfully received (i.e., the number of received degrees of freedom),and may provide further information that depends on the specific packetsthat have been received at the receiver although such furtherinformation is not essential.

As introduced above, packets that include the combinations of originalpackets generally also include information needed to determine thecoefficients used to combine the original packets, and informationneeded to identify which original packets were used in the combination(unless this set, such as all the packets of a block, is implicit). Insome implementations, the coefficients are explicitly represented in thecoded packets. In some embodiments, the coefficients are encoded withreference to shared information at the transmitter and the receiver. Forinstance, tables of pre-generated (e.g., random, pseudo random, orotherwise selected) coefficients, or sets of coefficients, may be storedand references into those tables are used to determine the values of thecoefficients. The size of such a table determines the number of paritypackets that can be generated while maintaining the linear independenceof the sets of coefficients. It should be understood that yet other waysmay be used to determine the coefficients.

Another feature of random linear codes is that packets formed as linearcombinations of data units may themselves be additively combined toyield combined linear combinations of data units. This process isreferred to in some instances as “recoding”, as distinct from decodingand then repeating encoding.

There are alternatives to the use of RLNC, which do not necessarilyachieve similar optimal (or provably optimum, or near optimal)throughput as RLNC, but that give excellent performance in somescenarios when implemented as described herein. For example, variousforms of parity check codes can be used. Therefore, it should beunderstood that RLNC, or any particular aspect of RLNC, is not anessential feature of all embodiments described in this document.

Batch Transmission

As introduced above, in at least some applications, data to betransmitted from a transmitter to a receiver forms a batch (i.e., asopposed to a continuous stream), with an example of a batch being a fileor a segment (e.g., a two second segment of multimedia) of a file.

In an embodiment of the PC-TCP modules, the batch is transferred fromthe transmitter to the receiver as a series of blocks, with each blockbeing formed from a series of information packets. In general, eachblock has the same number of information packets, however use of samesize blocks is not essential.

The transmitter PC-TCP module generally receives the data units from thesource application and forms the information packets of the successiveblocks of the batch. These information packets are queued at thetransmitter and transmitted on the channel to the receiver. In general,at the transmitter, the dequeueing and transmission of packets to thereceiver makes use of congestion control and/or rate control mechanismsdescribed in more detail below. The transmitter PC-TCP also retains theinformation packets (or sufficient equivalent information) to constructredundancy information for the blocks. For instance the transmitterPC-TCP buffers the information packets for each block for which thereremains the possibility of an unrecovered erasure of a packet duringtransit from the transmitter to the receiver.

In general, the receiver provides feedback to the transmitter. Variousapproaches to determining when to provide the feedback and whatinformation to provide with the feedback are described further below.The feedback provides the transmitter with sufficient information todetermine that a block has been successfully received and/orreconstructed at the receiver. When such success feedback for a blockhas been received, the transmitter no longer needs to retain theinformation packets for the block because there is no longer thepossibility that redundancy information for the block will need to besent to the receiver.

The feedback from the receiver to the transmitter may also indicate thata packet is missing. Although in some cases the indication that a packetis missing is a premature indication of an erasure, in this embodimentthe transmitter uses this missing feedback to trigger sending redundantinformation for a block. In some examples, the packets for a block arenumbered in sequence of transmission, and the feedback represents thehighest number received and the number of packets (i.e., the number ofdegrees of freedom) received (or equivalently the number of missingpackets or remaining degrees of freedom needed) for the block. Thetransmitter addresses missing packet feedback for a block through thetransmission of redundant repair blocks, which may be used by thereceiver to reconstruct the missing packets and/or original packets ofthe block.

As introduced above, for each block, the transmitter maintainssufficient information to determine the highest index of a packetreceived at the receiver, the number of missing packets transmittedprior to that packet, and the number of original or redundancy packetsafter the highest index received that have been transmitted (i.e., are“in flight” unless erased in transit) or queued for transmission at thetransmitter.

When the transmitter receives missing packet feedback for a block, ifthe number of packets for the block that are “in flight” or queue wouldnot be sufficient if received successfully (or are not expected to be inview of the erasure rate), the transmitter computes (or retrievesprecomputed) a new redundant packet for the block and queues it fortransmission. Such redundancy packets are referred to as repair packets.In order to reduce the delay in reconstructing a block of packets at thereceiver, the repair packets are sent preferentially to the informationpackets for later blocks. For instance, the repair packets are queued ina separate higher-priority queue that is used to ensure transmission ofrepair packets preferentially to the queue of information packets.

In some situations, feedback from the receiver may have indicated that apacket is missing. However, that packet may later arrive out of order,and therefore a redundant packet for that block that was earliercomputed and queued for transmission is no longer required to bedelivered to the receiver. If that redundant packet has not yet beentransmitted (i.e., it is still queued), that packet may be removed fromthe queue thereby avoiding wasted use of channel capacity for a packetthat will not serve to pass new information to the receiver.

In the approach described above, redundancy packets are sent as repairpackets in response to feedback from the receiver. In some examples,some redundancy packets are sent pre-emptively (i.e., as forward errorcorrection) in order to address possible packet erasures. One approachto send such forward error correction packets for each block. However,if feedback has already been received at the transmitter that asufficient number of original and/or coded packets for a block have beenreceived, then there is no need to send further redundant packets forthe block.

In an implementation of this approach, the original packets for all theblocks of the batch are sent first, while repair packets are beingpreferentially sent based on feedback from the receiver. After all theoriginal packets have been transmitted, and the queue of repair packetsis empty, the transmitter computes (or retrieves precomputed) redundancypackets for blocks for which the transmitter has not yet receivedfeedback that the blocks have been successfully received, and queuesthose blocks as forward error correction packets for transmission in thefirst queue. In general, because the repair blocks are sent with higherpriority that the original packets, the blocks for which successfeedback has not yet been received are the later blocks in the batch(e.g., a trailing sequence of blocks of the batch).

In various versions of this approach, the number and order oftransmission of the forward error correction packets are determined invarious ways. A first way uses the erasure rate to determine how manyredundant packets to transmit. One approach is to send at least oneredundant packet for each outstanding block. Another approach is to senda number of redundancy packets for each outstanding block so that basedon an expectation of the erasure rate of the packets that are queued andin flight for the block will yield a sufficient number of successfullyreceived packets in order to reconstruct the block. For example, if afurther n packets are needed to reconstruct a block (e.g., a number n<Npackets of the original N packets with N−n packets having been erased),then n+k packets are sent, for instance, with n+k≥n/E, where E is anestimate of the erasure rate on the channel.

Another way of determining the number and order of forward errorcorrection packets addresses the situation in which a block transmissiontime is substantially less than the round-trip-time for the channel.Therefore, the earliest of the blocks for which the transmitter has notreceived success feedback may in fact have the success feedback inflight from the receiver to the transmitter, and therefore sendingforward error correction packets may be wasteful. Similarly, even iffeedback indicating missing packet feedback for a block is receivedsufficiently early, the transmitter may still send a repair packetwithout incurring more delay in complete reconstruction of the entirebatch than would be achieved by forward error correction.

In an example, the number of forward error correction packets queued foreach block is greater for later blocks in the batch than for earlierones. A motivation for this can be understood by considering the lastblock of the batch where it should be evident that it is desirable tosend a sufficient number of forward error correction packets to ensurehigh probability of the receiver having sufficient information toreconstruct the block without the need from transmission of a repairpacket and the associated increase in latency. On the other hand, it ispreferable to send fewer forward error correction packets for theprevious (or earlier) block because in the face of missing packetfeedback from the receiver, the transmitter may be able to send a repairpacket before forward error correction packets for all the later blockshave been sent, thereby not incurring a delay in overall delivery of thebatch.

In one implementation, after all the original packets have been sent,and the transmitter is in the forward error correction phase in which itcomputes and sends the forward error correction packets, if thetransmitter receives a missing packet feedback from the receiver, itcomputes and sends a repair packet for the block in question (ifnecessary) as described above, and clears the entire queue of forwarderror correction packets. After the repair packet queue is again empty,the transmitter again computes and queues forward error correctionpackets for the blocks for which it has not yet received successfeedback. In an alternative somewhat equivalent implementation, ratherthan clearing the forward error correction queue upon receipt of amissing packet feedback, the transmitter removes forward errorcorrection packets from the queue as they are no longer needed based onfeedback from the receiver. In some examples, if success feedback isreceived for a block for which there are queued forward error correctionpackets, those forward error correction packets are removed from thequeue. In some examples, the feedback from the receiver may indicatethat some but not all of the forward error correction packets in thequeue are no longer needed, for example, because out-of-order packetswere received but at least some of the original packets are stillmissing.

An example of the way the transmitter determines how many forward errorcorrection packets to send is that the transmitter performs acomputation:

(N+g(i)−a _(i))/(1−p)−f _(i)

where

-   -   p=smoothed loss rate,    -   N=block size,    -   =block index defined as number of blocks from last block,    -   a_(i)=number of packets acked from block i,    -   f_(i)=packets in-flight from block i, and    -   g(i)=a decreasing function of i,        to determine the number of FEC packets for a block.

In some examples, g(i) is determined as a maximum of a configurableparameter, m and N−i. In some examples, g(i) is determined as N−p(i)where p is a polynomial, with integer rounding as needed

It should be understood that in some alternative implementations, atleast some forward error correction packets may be interspersed with theoriginal packets. For example, if the erasure rate for the channel isrelatively high, then at least some number of redundancy packets may beneeded with relatively high probability for each block, and there is anoverall advantage to preemptively sending redundant FEC packets as soonas possible, in addition to providing the mechanism for feedback basedrepair that is described above.

It should be also understood that use of subdivision of a batch intoblocks is not necessarily required in order to achieve the goal ofminimizing the time to complete reconstruction of the block at thereceiver. However, if the forward error correction is applied uniformlyto all the packets of the batch, then the preferential protection oflater packets would be absent, and therefore, latency caused by erasureof later packets may be greater than using the approach described above.However, alternative approaches to non-uniform forward error protection(i.e., introduction of forward error correction redundancy packets) maybe used. For example, in the block based approach described above,packets of the later blocks each contribute to a greater number offorward error correction packets than do earlier ones, and analternative approach to achieving this characteristic maybe to use anon-block based criterion to construction of the redundancy packets inthe forward error correction phase. However, the block based approachdescribed above has advantages of relative simplicity and generalrobustness, and therefore even if marginally “suboptimal” provides anoverall advantageous technical solution to minimizing the time tocomplete reconstruction within the constraint of throughput and erasureon the channel linking the transmitter and receiver.

Another advantage of using a block-based approach is that, for example,when a block within the batch, say the m^(th) block of M blocks of thebatch has an erasure, the repair packet that is sent from thetransmitter depends only on the N original packets of the m^(th) block.Therefore, as soon as the repair packet arrives, and the available(i.e., not erased) N−1 packets of the block arrive, the receiver has theinformation necessary to repair the block. Therefore, by constructingthe repair packet without contribution of packets in later blocks of thebatch, the latency of the reconstruction of the block is reduced.Furthermore, by having the repair packets depend on only N originalpackets, the computation required to reconstruct the packets of theblock is less than if the repair packets depend on more packets.

It should be understood that even in the block based transmission of abatch of packets, the blocks are not necessarily uniform in size, andare not necessarily disjoint. For example, blocks may overlap (e.g., by50%, 75%, etc.) thereby maintaining at least some of the advantages ofreduced complexity in reconstruction and reduced buffering requirementsas compared to treating the batch as one block. An advantage of suchoverlapping blocks may be a reduced latency in reconstruction becauserepair packets may be sent that do not require waiting for originalpackets at the receiver prior to reconstruction. Furthermore,non-uniform blocks may be beneficial, for example, to increase theeffectiveness of forward error correction for later block in a batch byusing longer blocks near the end of a batch as compared to near thebeginning of a batch.

In applications in which the entire batch is needed by the destinationapplication before use, low latency of reconstruction may be desirableto reduce buffering requirements in the PC-TCP module at the receiver(and at the transmitter). For example, all packets that may contributeto a later received repair packet are buffered for their potentialfuture use. In the block based approach, once a block is fullyreconstructed, then the PC-TCP module can deliver and discard thosepackets because they will not affect future packet reconstruction.

Although described as an approach to delivery of a batch of packets, theformation of these batches may be internal to the PC-TCP modules,whether or not such batches are formed at the software applicationlevel. For example, the PC-TCP module at the transmitter may receive theoriginal data units that are used to form the original packets via asoftware interface from the source application. The packets aresegmented into blocks of N packets as described above, and the packetsqueued for transmission. In one embodiment, as long as the sourceapplication provides data units sufficiently quickly to keep the queuefrom emptying (or from emptying for a threshold amount of time), thePC-TCP module stays in the first mode (i.e., prior to sending forwarderror correction packets) sending repair packets as needed based onfeedback information from the receiver. When there is a lull in thesource application providing data units, then the PC-TCP module declaresthat a batch has been completed, and enters the forward error correctionphase described above. In some examples, the batch formed by the PC-TCPmodule may in fact correspond to a batch of data units generated by thesource application as a result of a lull in the source applicationproviding data units to the PC-TCP module while it computes data unitsfor a next batch, thereby inherently synchronizing the batch processingby the source application and the PC-TCP modules.

In one such embodiment, the PC-TCP module remains in the forward errorcorrection mode for the declared batch until that entire batch has beensuccessfully reconstructed at the receiver. In another embodiment, ifthe source application begins providing new data units before thereceiver has provided feedback that the previous batch has beensuccessfully reconstructed, the transmitter PC-TCP module begins sendingoriginal packets for the next batch at a lower priority than repair orforward error correction packets for the previous batch. Such anembodiment may reduce the time to the beginning of transmission of thenext batch, and therefore reduces the time to successful delivery of thenext batch.

In the embodiments in which the source application does not necessarilyprovide the data in explicit batches, the receiver PC-TCP moduleprovides the data units in order to the destination application withoutnecessarily identifying the block or batch boundaries introduced at thetransmitter PC-TCP module. That is, in at least some implementations,the transmitter and receiver PC-TCP modules provide a reliable channelfor the application data units without exposing the block and batchstructure to the applications.

As described above for certain embodiments, the transmitter PC-TCPmodule reacts to missing packet feedback from the receiver PC-TCP moduleto send repair packets. Therefore, it should be evident that themechanism by which the receiver sends such feedback may affect theoverall behavior of the protocol. For example, in one example, thereceiver PC-TCP module sends a negative acknowledgment as soon as itobserves a missing packet. Such an approach may provide the lowestlatency for reconstruction of the block. However, as introduced above,missing packets may be the result of out-of-order delivery. Therefore, aless aggressive generation of missing packet feedback, for example, bydelay in transmission of a negative acknowledgment, may reduce thetransmission of unnecessary repair packets with only a minimal increasein latency in reconstruction of that block. However, such delay insending negative acknowledgements may have an overall positive impact onthe time to successfully reconstruct the entire block because laterblocks are not delayed by unnecessary repair packets. Alternativeapproaches to generation of acknowledgments are described below.

In some embodiments, at least some of the determination of when to sendrepair packets is performed at the transmitter PC-TCP. For example, thereceiver PC-TCP module may not delay the transmission of missing packetfeedback, and it is the transmitter PC-TCP module that delays thetransmission of a repair packet based on its weighing of the possibilityof the missing packet feedback being based on out-of-order delivery asopposed to erasure.

Protocol Parameters

Communication between two PC-TCP endpoints operates according toparameters, some of which are maintained in common by the endpoints, andsome of which are local to the sending and/or the receiving endpoint.Some of these parameters relate primarily to forward error correctionaspects of the operation. For example, such parameters include thedegree of redundancy that is introduced through the coding process. Asdiscussed below, further parameters related to such coding relate to theselection of packets for use in the combinations. A simple example ofsuch selection is segmentation of the sequence of input data units into“frames” that are then independently encoded. In addition to the numberof such packets for combination (e.g., frame length), other parametersmay relate to overlapping and/or interleaving of such frames of dataunits and/or linear combinations of such data units.

Further parameters relate generally to transport layer characteristicsof the communication approach. For example, some parameters relate tocongestion avoidance, for example, representing a size of a window ofunacknowledged packets, transmission rate, or other characteristicsrelated to the timing or number of packets sent from the sender to thereceiver of the PC-TCP communication.

As discussed further below, communication parameters (e.g., codingparameters, transport parameters) may be set in various ways. Forexample, parameters may be initialized upon establishing a sessionbetween two PC-TCP endpoints. Strategies for setting those parametersmay be based on various sources of information, for example, accordingto knowledge of the communication path linking the sender and receiver(e.g., according to a classification of path type, such as 3G wirelessversus cable modem), or experienced communication characteristics inother sessions (e.g., concurrent or prior sessions involving the samesender, receiver, communication links, intermediate nodes, etc.).Communication parameters may be adapted during the course of acommunication session, for example, in response to observedcommunication characteristics (e.g., congestion, packet loss, round-triptime, etc.)

Transmission Control

Some aspects of the PC-TCP approaches relate to control of transmissionof packets from a sender to a receiver. These aspects are generallyseparate from aspects of the approach that determine what is sent in thepackets, for example, to accomplish forward error correction,retransmission, or the order in which the packets are sent (e.g.,relative priority of forward error correction packets versionretransmission packets). Given a queue of packets that are ready fortransmission from the sender to the receiver, these transmission aspectsgenerally relate to flow and/or congestion control.

Congestion Control

Current variants of TCP, including binary increase congestion control(BIC) and cubic-TCP, have been proposed to address the inefficiencies ofclassical TCP in networks with high losses, large bandwidths and longround-trip times. BIC-TCP and CUBIC algorithms have been used because oftheir stability. After a backoff, BIC increases the congestion windowlinearly then logarithmically to the window size just before backoff(denoted by W_(max)) and subsequently increases the window in ananti-symmetric fashion exponentially then linearly. CUBIC increases thecongestion window following backoff according to a cubic function withinflection point at W_(max). These increase functions cause thecongestion window to grow slowly when it is close to W_(max), promotingstability. On the other hand, other variants such as HTCP and FAST TCPhave the advantage of being able to partially distinguish congestion andnon-congestion losses through the use of delay as a congestion signal.

An alternative congestion control approach is used in at least someembodiments. In some such embodiments, we identify a concave portion ofthe window increase function as W_(concave)(t)=W_(max)+c₁(t−k)³ and aconvex portion of the window increase function asW_(convex)(t)=W_(max)+c₂(t−k)³ where c₁ and c₂ are positive tunableparameters and

$k = \sqrt[3]{( {( {{W\_ max} - W} )/c_{1}} )}$

and W is the window size just after backoff.

This alternative congestion control approach can be flexibly tuned fordifferent scenarios. For example, a larger value of c₁ causes thecongestion window to increase more rapidly up to W_(max) and a largevalue of c₂ causes the congestion window to increase more rapidly beyondW_(max).

Optionally, delay is used as an indicator to exit slow start and move tothe more conservative congestion avoidance phase, e.g. when a smoothedestimate of RTT exceeds a configured threshold relative to the minimumobserved RTT for the connection. We can also optionally combine theincrease function of CUBIC or other TCP variants with the delay-basedbackoff function of HTCP.

In some embodiments, backoff is smoothed by allowing a lower rate oftransmission until the number of packets in flight decreases to the newwindow size. For instance, a threshold, n, is set such that once npackets have been acknowledged following a backoff, then one packet isallowed to be sent for every two acknowledged packets, which is roughlyhalf of the previous sending rate. This is akin to a hybrid window andrate control scheme.

Transmission Rate Control Pacing Control by Sender

In at least some embodiments, pacing is used to regulate and/or spreadout packet transmissions, making the transmission rate less bursty.While pacing can help to reduce packet loss from buffer overflows,previous implementations of pacing algorithms have not shown clearadvantages when comparing paced TCP implementations to non-paced TCPimplementations. However, in embodiments where the data packets arecoded packets as described above, the combination of packet coding andpacing may have advantages. For example, since one coded packet may beused to recover multiple possible lost packets, we can use coding tomore efficiently recover from any spread out packet losses that mayresult from pacing. In embodiments, the combination of packet coding andpacing may have advantages compared to uncoded TCP with selectiveacknowledgements (SACK).

Classical TCP implements end-to-end congestion control based onacknowledgments. Variants of TCP designed for high-bandwidth connectionsincrease the congestion window (and consequently the sending rate)quickly to probe for available bandwidth but this can result in burstsof packet losses when it overshoots, if there is insufficient bufferingin the network.

A number of variants of TCP use acknowledgment feedback to determineround-trip time and/or estimate available bandwidth, and they differ inthe mechanisms with which this information is used to control thecongestion window and/or sending rate. Different variants have scenariosin which they work better or worse than others.

In one general approach used in one or more embodiments, a communicationprotocol may use smoothed statistics of intervals betweenacknowledgments of transmitted packets (e.g., a smoothed “ack interval”)to guide a transmission of packets, for example, by controllingintervals (e.g., an average interval or equivalently an averagetransmission rate) between packet transmissions. Broadly, this guidingof transmission intervals is referred to herein as “pacing”.

In some examples, the pacing approach is used in conjunction with awindow-based congestion control algorithm. Generally, the congestionwindow controls the number of unacknowledged packets that can be sent,in some examples using window control approaches that are the same orsimilar to those used in known variants of the Transmission ControlProtocol (TCP). In embodiments, the window control approach is based onthe novel congestion control algorithms described herein.

A general advantage of one or more aspects is to improve functioning ofa communication system, for instance, as measured by total throughput,or delay and/or variation in delay. These aspects address a technicalproblem of congestion, and with it packet loss, in a network by using“pacing” to reduce that congestion.

An advantage of this aspect is that the separate control of pacing canprevent packets in the congestion window from being transmitted toorapidly compared to the rate at which they are getting through to theother side. Without separate pacing control, at least some conventionalTCP approaches would permit bursts of overly rapid transmission ofpackets, which might result in packet loss at an intermediate node onthe communication path. These packet losses may be effectivelyinterpreted by the protocol as resulting from congestion, resulting inthe protocol reducing the window size. However, the window size may beappropriate, for example, for the available bandwidth and delay of thepath, and therefore reducing the window size may not be necessary. Onthe other hand, reducing the peak transmission rate can have the effectof avoiding packet loss, for example, by avoiding overflow ofintermediate buffers on the path.

Another advantage of at least some implementations is prevention oflarge bursts of packet losses under convex window increase functions forhigh-bandwidth scenarios, by providing an additional finer level ofcontrol over the transmission process.

At least some implementations of the approach can leverage theadvantages of existing high-bandwidth variants of TCP such as H-TCP andCUBIC, while preventing large bursts of packet losses under their convexwindow increase functions and providing a more precise level of control.For example, pacing control may be implemented to pace the rate ofproviding packets from the existing TCP procedure to the channel, withthe existing TCP procedure typically further or separately limiting thepresentation of packets to the communication channel based, forinstance, on its window-based congestion control procedure.

In practice, a particular example in which separating pacing from windowcontrol has been observed to significantly outperform conventional TCPon 4G LTE.

Referring to FIG. 158, in one example, a source application 1010 passesdata to a destination application 1090 over a communication channel1050. Communication from the source application 1010 passes to atransport layer 1020, which maintains a communication session with acorresponding transport layer 1080 linked to the destination application1090. In general, the transport layers may be implemented as softwarethat executes on the same computer as their corresponding applications,however, it should be recognized that, for instance through the use ofproxy approaches, the applications and the transport layer elements thatare shown may be split over separate coupled computers. In embodiments,when a proxy is running on a separate machine or device from theapplication, the application may use the transport layer on its machineto communicate with the proxy layer.

In FIG. 158, the transport layer 1020 at the source application includesa window control and retransmission element 1030. In someimplementations, this element implements a conventional TransportControl Protocol (TCP) approach, for instance, implementing H-TCP orCUBIC approaches. In other implementations, this element implements thenovel congestion control algorithms described herein. The transportlayer 1080 at the destination may implement a corresponding element1060, which may provide acknowledgements of packets to the windowcontrol and retransmission element 1030 at the source. In general,element 1030 may implement a window-based congestion control approachbased on acknowledgements that are received at the destination, howeverit should be understood that no particular approach to window control isessential, and in some implementations, element 1030 can be substitutedwith another element that implements congestion control using approachesother than window control.

Functionally, one may consider two elements of the protocol as beingloss recovery and rate/congestion control. Loss recovery can beimplemented either using conventional retransmissions or using coding oras a combination of retransmission and coding. Rate/congestion controlmay aim to avoid overrunning the receiver and/or the available channelcapacity, and may be implemented using window control with or withoutpacing, or direct rate control.

The channel 1050 coupling the transport layers in general may includelower layer protocol software at the source and destination, and aseries of communication links coupling computers and other network nodeson a path from the source to the destination.

As compared to conventional approaches, as shown in FIG. 136, a ratecontrol element 1040 may be on the path between the window control andretransmission element 1030 and the channel 1050. This rate controlelement may monitor acknowledgements that are received from thedestination, and may pass them on to the window control andretransmission element 1030, generally without delay. The rate controlelement 1040 receives packets for transmission on the channel 1050 fromthe window control and retransmission element 1030, and either passesthem directly to the channel 1050, or buffers them to limit a rate oftransmission onto the channel. For example, the rate control element1040 may require a minimum interval between successive packets, or maycontrol an average rate over multiple packets.

In embodiments, the acks that are transmitted on a return channel, fromthe destination to the source, may also be paced, and may also utilizecoding to recover from erasures and bursty losses. In embodiments,packet coding and transmission control of the acks may be especiallyuseful if there is congestion on the return channel.

In one implementation, the rate control element 1040 may maintain anaverage (i.e., smoothed) inter-packet delivery interval, estimated basedon the acknowledgement intervals (accounting for the number of packetsacknowledged in each ack). In some implementations this averaging may becomputed as a decaying average of past sample inter-arrival times. Thiscan be refined by incorporating logic for discarding large sample valuesbased on the determination of whether they are likely to have resultedfrom a gap in the sending times or losses in the packet stream, and bysetting configurable upper and lower limits on the estimated intervalcommensurate with particular characteristics of different knownnetworks. The rate control element 1040 may then use this smoothedinter-acknowledgement time to set a minimum inter-transmission time, forexample, as a fraction of the inter-acknowledgement time. This fractioncan be increased with packet loss and with rate of increase of RTT(which may be indicators that the current sending rate may be too high),and decreased with rate of decrease of RTT under low loss, e.g. using acontrol algorithm such as proportional control whose parameters can beadjusted to trade off between stability and responsiveness to change.Upper and lower limits on this fraction can be made configurableparameters, say 0.2 and 0.95. Transmission packets are then limited tobe presented to the channel 1050 with inter-transmission times of atleast this set minimum. In other implementations inter-transmissionintervals are controlled to maintain a smoothed average interval or ratebased on a smoothed inter-acknowledgement interval or rate.

In addition to the short timescale adjustments of the pacing intervalwith estimated delivery interval, packet loss rate and RTT describedabove, there can also be a longer timescale control loop that modulatesthe overall aggressiveness of the pacing algorithm based on a smoothedloss rate calculated over a longer timescale, with, a higher loss rateindicating that pacing may be too aggressive. The longer timescaleadjustment can be applied across short duration connections by havingthe client maintain state across successive connections and includeinitializing information in subsequent connection requests. This longertimescale control may be useful for improving adaptation to diversenetwork scenarios that change dynamically on different timescales.

Referring to FIG. 159, in some implementations, the communicationchannel 1050 spans multiple nodes 1161, 1162 in one or aninterconnection of communication networks 1151, 1152. In FIG. 137, thesource application 1010 is illustrated as co-resident with the transportlayer 1020 on a source computer 1111, and similarly, the transport layer1080 is illustrated as co-resident on a destination computer 1190 withthe destination application 1090.

It should be recognized that although the description above focuses on asingle direction of communication, in general, a bidirectionalimplementation would include a corresponding path from the destinationapplication to the source application. In some implementations, bothdirections include corresponding rate control elements 1040, while inother applications, only one direction (e.g., from the source to thedestination application) may implement the rate control. For example,introduction of the rate control element 1040 at a server, or anotherdevice or network node on the path between the source application andthe transport layer 1080 at the destination, may not requiremodification of the software at the destination.

Pacing by Receiver

As described above, the sender can use acks to estimate therate/interval with which packets are reaching the receiver, the lossrate and the rate of change of RTT, and adjust the pacing intervalaccordingly. However, this estimated information may be noisy if acksare lost or delayed. On the other hand, such information can beestimated more accurately at the receiver with OWTT in place of RTT. Bybasing the pacing interval on the rate of change of OWTT rather than itsactual value, the need for synchronized clocks on sender and receivermay be obviated. The pacing interval can be fed back to the sender byincluding it as an additional field in the acks. The choice as towhether the pacing calculations are done at the sender or the receiver,or done every n packets rather than upon every packet reception, mayalso be affected by considerations of sender/receiver CPU/load.

Error Control

Classical TCP performs poorly on networks with packet losses. Congestioncontrol can be combined with coding such that coded packets are sentboth for forward error correction (FEC) to provide protection against ananticipated level of packet loss, as well as for recovering from actuallosses indicated by feedback from the receiver.

While the simple combination of packet coding and congestion control hasbeen suggested previously, the prior art does not adequately account fordifferences between congestion-related losses, bursty and/or randompacket losses. Since congestion-related loss may occur as relativelyinfrequent bursts, it may be inefficient to protect against this type ofloss using FEC.

In at least some embodiments, the rates at which loss events occur areestimated. A loss event may be defined as either an isolated packet lossor a burst of consecutive packet losses. In some examples, the sourcePC-TCP may send FEC packets at the estimated rate of loss events, ratherthan the estimated rate of packet loss. This embodiment is an efficientway to reduce non-useful FEC packets, since it may not bedisproportionately affected by congestion-related loss.

In an exemplary embodiment, the code rate and/or packet transmissionrate of FEC can be made tunable in order to trade-off between the usefulthroughput seen at the application layer (also referred to as goodput)and recovery delay. For instance, the ratio of the FEC rate to theestimated rate of loss events can be made a tunable parameter that isset with a priori knowledge of the underlying communications paths ordynamically adjusted by making certain measurements of the underlyingcommunications paths.

In another exemplary embodiment, the rate at which loss bursts of up toa certain length occur may be estimated, and appropriate burst errorcorrecting codes for FEC, or codes that correct combinations of burstand isolated errors, may be used.

In another exemplary embodiment, the FEC for different blocks can beinterleaved to be more effective against bursty loss.

In other exemplary embodiments, data packets can be sent preferentiallyover FEC packets. For instance, FEC packets can be sent at a configuredrate or estimated loss rate when there are no data packets to be sent,and either not sent or sent at a reduced rate when there are datapackets to be sent. In one implementation, FEC packets are placed in aseparate queue which is cleared when there are data packets to be sent.

In other exemplary embodiments, the code rate/amount of FEC in eachblock and/or the FEC packet transmission rate can be made a tunablefunction of the block number and/or the number of packets in flightrelative to the number of unacknowledged degrees of freedom of theblock, in addition to the estimated loss rate. FEC packets for laterblocks can be sent preferentially over FEC for earlier blocks, so as tominimize recovery delay at the end of a connection, e.g., the number ofFEC packets sent from each block can be a tunable function of the numberof blocks from the latest block that has not been fully acknowledged.The sending interval between FEC packets can be an increasing functionof the number of packets in flight relative to the number ofunacknowledged degrees of freedom of the corresponding block, so as totrade-off between sending delay and probability of losing FEC packets inscenarios where packet loss probability increases with transmissionrate.

In other exemplary embodiments, a variable randomly chosen fraction ofthe coding coefficients of a coded packet can be set to 1 or 0 in orderto reduce encoding complexity without substantially affecting erasurecorrection performance. In a systematic code, introducing 0 coefficientsonly after one or more densely coded packets (i.e. no or few 0coefficients) may be important for erasure correction performance. Forinstance, an initial FEC packet in a block could have each coefficientset to 1 with probability 0.5 and to a uniformly random value from thecoding field with probability 0.5. Subsequent FEC packets in the blockcould have each coefficient set to 0 with probability 0.5 and touniformly random value with probability 0.5.

Packet Reordering

As introduced above, packets may be received out of order on somenetworks, for example, due to packets traversing multiple paths,parallel processing in some networking equipment, reconfiguration of apath (e.g., handoff in cellular networks). Generally, conventional TCPreacts to out of order packets by backing off the size of the congestionwindow. Such a backoff may unnecessarily hurt performance if there is nocongestion necessitating a backoff.

In some embodiments, in an approach to handling packet reordering thatdoes not result from congestions, a receiver observing a gap in thesequence numbers of its received packets may delay sending anacknowledgment for a limited time. When a packet is missing, thereceiver does not immediately know if the packet has been lost (erased),or merely reordered. The receiver delays sending an acknowledgement thatindicates the gap to see if the gap is filled by subsequent packetarrivals. In some examples, upon observing a gap, the receiver starts afirst timer for a configurable “reordering detection” time interval,e.g. 20 ms. If a packet from the gap is subsequently received withinthis time interval, the receiver starts a second timer for aconfigurable “gap filling” time interval, e.g. 30 ms. If the first timeror the second timer expire prior to the gap being filled, anacknowledgement that indicates the gap is sent to the source.

Upon receiving the acknowledgment that indicates the gap in receivedpackets the source, in at least some embodiments, the sender determineswhether a repair packet should be sent to compensate for the gap in thereceived packets, for example, if a sufficient number of FEC packetshave not already been sent.

In another aspect, a sender may store relevant congestion control stateinformation (including the congestion window) prior to backoff, and arecord of recent packet losses. If the sender receives an ack reportinga gap/loss and then subsequently one or more other acks reporting thatthe gap has been filled by out of order packet receptions, any backoffcaused by the earlier ack can be reverted by restoring the stored statefrom before backoff.

In another aspect, a sender observing a gap in the sequence numbers ofits received acks may delay congestion window backoff for a limitedtime. When an ack is missing, the sender does not immediately know if apacket has been lost or if the ack is merely reordered. The senderdelays backing off its congestion window to see if the gap is filled bysubsequent ack arrivals. In some examples, upon observing a gap, thesender starts a first timer for a configurable “reordering detection”time interval, e.g. 20 ms. If an ack from the gap is subsequentlyreceived within this time interval, the sender starts a second timer fora configurable “gap filling” time interval, e.g. 30 ms. If the firsttimer or the second timer expires prior to the gap being filled,congestion window backoff occurs.

In some examples, instead of using time intervals, packet sequencenumbers are used. For example, sending of an ack can be delayed until apacket which is a specified number of sequence numbers ahead of thereference lost packet is received. Similarly, backing off can be delayeduntil an acknowledgment of a packet which is a specified number ofsequence numbers ahead of the reference lost packet is received. In someexamples, these approaches have the advantage of being able to take intoaccount subsequently received/acknowledged reordered packets by shiftingthe sequence number of the reference lost packet as holes in the packetsequence get filled.

These methods for correcting packet reordering may be especially usefulfor multipath versions of the protocol, where there may be a largeamount of reordering.

Acknowledgements Delayed Acknowledgements

In at least some implementations, conventional TCP sends oneacknowledgment for every two data packets received. Such delayed ackingreduces ack traffic compared to sending an acknowledgment for every datapacket. This reduction in ack traffic is particularly beneficial whenthere is contention on the return channel, such as in Wi-Fi networks,where both data and ack transmissions contend for the same channel.

It is possible to reduce ack traffic further by increasing the ackinterval to a value n>2, i.e. sending one acknowledgment for every ndata packets. However, reducing the frequency with which acks arereceived by the sender can cause delays in transmission (when thecongestion window is full) or backoff (if feedback on losses isdelayed), which can hurt performance.

In one aspect, the sender can determine whether, or to what extent,delayed acking should be allowed based in part on its remainingcongestion window (i.e. its congestion window minus the number ofunacknowledged packets in flight), and/or its remaining data to be sent.For example, delayed acking can be disallowed if there is any packetloss, or if the remaining congestion window is below some (possiblytunable) threshold. Alternatively, the ack interval can be reduced withthe remaining congestion window. As another example, delayed acking canbe allowed if the amount of remaining data to be sent is smaller thanthe remaining congestion window, but disallowed for the last remainingdata packet so that there is no delay in acknowledging the last datapacket. This information can be sent in the data packets as a flagindicating whether delayed acking is allowed, or for example, as aninteger indicating the allowed ack interval.

Using relevant state information at the sender to influence delayedacking may allow an increase in the ack interval beyond the conventionalvalue of 2, while mitigating the drawbacks described above that a largerack interval across the board might have.

To additionally limit the ack delay, each time an ack is sent, a delayedack timer can be set to expire with a configured delay, say 25 ms. Uponexpiration of the timer, any data packets received since the last ackmay be acknowledged, even if fewer packets than the ack interval n havearrived. If no packets have been received since the last ack, an ack maybe sent upon receipt of the next data packet.

Parameter Control Initialization

In some embodiments, to establish a session parameters for the PC-TCPmodules are set to a predefine set of default parameters. In otherembodiments, approaches that attempt to select better initial parametersare used. Approaches include use of parameter values from otherconcurrent or prior PC-TCP sessions, parameters determined fromcharacteristics of the communication channel, for example, selected fromstored parameters associated with different types of channels, orparameters determined by the source or destination application accordingto the nature of the data to be transported (e.g., batch versus stream).

Tunable Coding

Referring to FIG. 160, in an embodiment in which parameters are “tuned”(e.g., through feedback from a receiver or on other considerations) aserver application 2411 is in communication with a client application2491 via a communication channel 2452. In one example, the serverapplication 2411 may provide a data stream encoding multimedia content(e.g., a video) that is accepted by the client application 2491, forexample, for presentation to a user of the device on which the clientapplication is executing. The channel 2452 may represent what istypically a series of network links, for example including links of oneor more types, including:

a link traversing private links on a server local area network,

a link traversing the public Internet,

a link traversing a fixed (i.e., wireline) portion of a cellulartelephone network,

and a link traversing a wireless radio channel to the user's device(e.g., a cellular telephone channel or satellite link or wireless LAN).

The channel 2452 may be treated as carrying a series of data units,which may but do not necessarily correspond directly to InternetProtocol (IP) packets. For example, in some implementations multipledata units are concatenated into an IP packet, while in otherimplementations, each data unit uses a separate IP packet or only partof an IP packet. It should be understood that in yet otherimplementations, the Internet Protocol is not used—the techniquesdescribed below do not depend on the method of passing the data unitsover the channel 2452.

A transmitter 2421 couples the server application 2411 to the channel2452, and a receiver 2481 couples the channel 2452 to the clientapplication 2491. Generally, the transmitter 2421 accepts input dataunits from the server application 2481. In general, these data units arepassed over the channel 2452, as well as retained for a period of timein a buffer 2423. From time to time, an error control (EC) component2425 may compute a redundancy data unit from a subset of the retainedinput data units in the buffer 2423, and may pass that redundancy dataunit over the channel 2452. The receiver 2481 accepts data units fromthe channel 2452. In general, the channel 2452 may erase and reorder thedata units. Erasures may correspond to “dropped” data units that arenever received at the receiver, as well as corrupted data units that arereceived, but are known to have irrecoverable errors, and therefore aretreated for the most part as dropped units. The receiver may retain ahistory of received input data units and redundancy data units in abuffer 2483. An error control component 2485 at the receiver 2481 mayuse the received redundancy data units to reconstruct erased input dataunits that may be missing in the sequence received over the channel. Thereceiver 2481 may pass the received and reconstructed input data unitsto the client application. In general, the receiver may pass these inputdata units to the client application in the order they were received atthe transmitter.

In general, if the channel has no erasures or reordering, the receivercan provide the input data units to the client application with delayand delay variation that may result from traversal characteristics ofthe channel. When data units are erased in the channel 2452, thereceiver 2481 may make use of the redundancy units in its buffer 2483 toreconstruct the erased units. In order to do so, the receiver may haveto wait for the arrival of the redundancy units that may be useful forthe reconstruction. The way the transmitter computes and introduces theredundancy data units generally affects the delay that may be introducedto perform the reconstruction.

The way the transmitter computes and introduces the redundancy dataunits as part of its forward error correction function can also affectthe complexity of the reconstruction process at the receiver, and theutilization of the channel. Furthermore, regardless of the nature of theway the transmitter introduces the redundancy data units onto thechannel, statistically there may be erased data units for which there isinsufficient information in the redundancy data units to reconstruct theerased unit. In such cases, the error control component 2485 may requesta retransmission of information from the error control component 2425 ofthe transmitter 2421. In general, this retransmitted information maytake the form of further redundancy information that depends on theerased unit. This retransmission process introduces a delay before theerased unit is available to the receiver. Therefore, the way thetransmitter introduces the redundancy information also affects thestatistics such as how often retransmission of information needs to berequested, and with it the delay in reconstructing the erased unit thatcannot be reconstructed using the normally introduced redundancyinformation.

In some embodiments, the error control component 2485 may provideinformation to the error control component 2425 to affect the way thetransmitter introduces the redundancy information. In general, thisinformation may be based on one or more of the rate of (or moregenerally the pattern of) erasures on units on the channel, rate of (ormore generally timing pattern of) and the state of the available unitsin the buffer 2483 and/or the state of unused data in the clientapplication 2491. For example, the client application may provide a“play-out time” (e.g., in milliseconds) of the data units that thereceiver has already provided to the client application such that if thereceiver were to not send any more units, the client application wouldbe “starved” for input units at that time. Note that in otherembodiments, rather than or in addition to receiving information fromthe receiver, the error control component 2425 at the transmitter mayget feedback from other places, for example, from instrumented nodes inthe network that pass back congestion information.

Referring to FIG. 161, a set of exemplary ways that the transmitterintroduces the redundancy data units into the stream of units passedover the channel makes use of alternating runs of input data units andredundancy data units. In FIG. 161, the data units that are “in flight”on the channel 2452 are illustrated passing from left to right in thefigure. The transmitter introduces the units onto the channel assequences of p input units alternating with sequences of q redundancyunits. Assuming that the data units are the same sizes, this correspondsto a rate R=p/(p+q) code. In an example with p=4 and q=2 and the codehas rate R=⅔.

In a number of embodiments the redundancy units are computed as randomlinear combinations of past input units. Although the description belowfocuses on such approaches, it should be understood that the overallapproach is applicable to other computations of redundancy information,for example, using low density parity check (LDPC) codes and other errorcorrection codes. In the approach shown in FIG. 161, each run of qredundancy units is computed as a function of the previous D inputunits, where in general but not necessarily D>p. In some cases, the mostrecent d data units transmitted are not used, and therefore theredundancy data units are computed from a window of D−d input dataunits. In FIG. 161, d=2, D=10, and D−d=8. Note that because D−d>p, thewindows of input data units used for computation of the successive runsof redundancy units overlap, such that any particular input data unitwill in general contribute to redundancy data units in more than one ofthe runs of q units on the channel.

In FIG. 161, as well as in FIGS. 162-163 discussed below, buffered inputdata units (i.e., in buffer 2423 shown in FIG. 160) are shown on theleft with time running from the bottom (past) to the top (future), witheach set of D-d units used to compute a run of q redundant unitsillustrated with arrows. The sequence of transmitted units, consistingof runs of input data units alternating with runs of redundant units, isshown with time running from right to left (i.e., later packets on theleft). Data units that have been received and buffered at the receiverare shown on the right (oldest on the bottom), redundant units computedfrom runs of D-d input units indicated next to arrows representing theranges of input data units used to compute those data units. Data unitsand ranges of input data units that have not yet been received areillustrated using dashed lines.

FIGS. 162 and 163 show different selections of parameters. In FIG. 162,p=2 and q=1 and the code has a rate R=⅔, which is the same rate at theselection of parameters in FIG. 161. Also as in the FIG. 161 selection,d=2, D=10, and D-d=8. Therefore, a difference between FIG. 161 and FIG.162 is not necessarily a degree of forward error protection (althoughthe effect of burst erasures may be somewhat different in the twocases). More importantly, the arrangement in FIG. 162 generally providesa lower delay from the time of an erased data unit to the arrival ofredundancy information to reconstruct that unit, as compared to thearrangement in FIG. 161. On the other hand, the complexity of processingat the receiver may be greater in the arrangement of FIG. 162 ascompared to the arrangement of FIG. 160, in part because redundancyunits information uses multiple different subsets of the input dataunits, which may require more computation when reconstructing an eraseddata unit. Turning to FIG. 163, at another extreme, a selection ofparameters uses longer blocks with a selection D=8 and q=4. Again, thiscode has a rate R=⅔. In general, this selection of parameters will incurgreater delay in reconstruction of an erased data unit as compared tothe selections of parameters shown in FIGS. 161 and 162. On the otherhand, reconstruction of up to four erasures per block of D=8 input dataunits is relatively less complex than would be required by theselections shown in FIGS. 161 and 162.

For a particular rate of code (e.g., rate R=⅔), in an example, feedbackreceived may result in changes of the parameters, for example, between(p,q)=(2,1) or (4,2) or (8,4) depending on of the amount of databuffered at the receiver, and therefore depending on the tolerance ofthe receiver to reconstruction delay.

Note that it is not required that q=p(1−R)/R is an integer, as it is inthe examples shown in FIGS. 161-163. In some embodiments, the length ofthe run of redundant units varies between q=┌p (1−R)/R┐ and q=└p(1−R)/R┘so that the average is ave(q)=p(1−R)/R.

In a variant of the approach described above, different input data unitshave different “priorities” or “importances” such that they areprotected to different degrees than other input data units. For example,in video coding, data units representing an independently coded videoframe may be more important than data units representing adifferentially encoded video frame. For example, if the priority levelsare indexed i=1, 2, . . . , then a proportion ρ_(i)≤1, whereΣ_(i)ρ_(i)=1, of the redundancy data units may be computed using dataunits with priority ≤i. For example, for a rate R code, with blocks ofinput data units of length p, on average A ρ_(i)(1−R)/R redundancy dataunits per block are computed from input data units with priority ≤i.

The value of D should generally be no more than the target playout delayof the streaming application minus an appropriate margin forcommunication delay variability. The playout delay is the delay betweenthe time a message packet is transmitted and the time it should beavailable at the receiver to produce the streaming application output.It can be expressed in units of time, or in terms of the number ofpackets transmitted in that interval. D can be initially set based onthe typical or desired playout delay of the streaming application, andadapted with additional information from the receiver/application.Furthermore, choosing a smaller value reduces the memory and complexityat the expense of erasure correction capability.

The parameter d specifies the minimum separation between a messagepacket and a parity involving that message packet. Since a parityinvolving a message packet that has not yet been received is not usefulfor recovering earlier message packets involved in that parity, settinga minimum parity delay can improve decoding delay when packet reorderingis expected/observed to occur, depending partly also on the parityinterval.

Referring to FIG. 164, in an example implementation making use of theapproaches described above, the server application 2411 is hosted withthe transmitter 2421 at a server node 810, and the client application2491 is hosted at one or a number of client nodes 891 and 892. Althougha wide variety of types of data may be transported using the approachesdescribed above, one example is streaming of encoded multimedia (e.g.,video and audio) data. The communication channel 2452 (see FIG. 160) ismade up in this illustration as a path through one or more networks851-852 via nodes 861-862 in those respective networks. In someimplementations, the receiver is hosted at a client node 891 beinghosted on the same device as the client application 490.

Cross-Session Parameter Control

In some embodiments, the control of transport layer sessions usesinformation across connections, for example, across concurrent sessionsor across sessions occurring at different times.

Standard TCP implements end-to-end congestion control based onacknowledgments. A new TCP connection that has started up but not yetreceived any acknowledgments uses initial configurable values for thecongestion window and retransmission timeout. These values may be tunedfor different types of network settings.

Some applications, for instance web browser applications, may usemultiple connections between a client application (e.g., the browser)and a server application (e.g., a particular web server application at aparticular server computer). Conventionally, when accessing theinformation to render a single web “page”, the client application maymake many separate TCP sessions between the client and server computers,and using conventional TCP control, each session is controlledsubstantially independently. This independent control includes separatecongestion control.

One approach to addressing technical problems that are introduced byhaving such multiple sessions is the SPDY Protocol (see, e.g., SPDYProtocol-Draft 3.1, accessible athttp://www.chromium.org/spdy/spdy-protocol/spdy-protocol-draft3-1). TheSPDY protocol is an application layer protocol that manipulates HTTPtraffic, with particular goals of reducing web page load latency andimproving web security. Generally, SPDY effectively provides a tunnelfor the HTTP and HTTPS protocols. When sent over SPDY, HTTP requests areprocessed, tokenized, simplified and compressed. The resulting trafficis then sent over a single TCP session, thereby avoiding problems andinefficiencies involved in use of multiple concurrent TCP sessionsbetween a particular client and server computer.

In a general aspect, a communication system maintains informationrelated to communication between computers or network nodes. Forexample, the maintained information can include bandwidth to and/or fromthe other computer, current or past congestion window sizes, pacingintervals, packet loss rates, round-trip time, timing variability, etc.The information can include information for currently active sessionsand/or information about past sessions. One use of the maintainedinformation may be to initialize protocol parameters for a new sessionbetween computers for which information has been maintained. Forexample, the congestion window size or a pacing rate for a new TCP orUDP session may be initialized based on the congestion window size,pacing interval, round-trip time and loss rate of other concurrent orpast sessions.

Referring to FIG. 165, communication system 1200 maintains informationregarding communication sessions between endpoints. For example, thesecommunication sessions pass via a network 1250, and may pass between aserver 1210, or a proxy 1212 serving one or more servers 1214, and aclient 1290. In various embodiments, this information may be saved invarious locations. In some implementations, a client 1290 maintainsinformation about current or past connections. This information may bespecific to a particular server 1210 or proxy 1212. This information mayalso include aggregated information. For example, in the case of asmartphone on a cellular telephone network, some of the information maybe generic to connections from multiple servers and may representcharacteristics imposed by the cellular network rather than a particularpath to a server 1210. In some implementations, a server 1210 or proxy1212 may maintain the information based on its past communication withparticular clients 1290. In some examples, the clients and servers mayexchange the information such that is it distributed throughout thesystem 1200. In some implementations, the information may be maintainedin databases that are not themselves endpoints for the communicationsessions. For instance, it may be beneficial for a client withoutrelevant stored information to retrieve information from an externaldatabase.

In one use scenario, when a client 1290 seeks to establish acommunication session (e.g., a transport layer protocol session), itconsults its communication information 1295 to see if it has currentinformation that is relevant to the session it seeks to establish. Forexample, the client may have other concurrent sessions with a serverwith which it wants to communicate, or with which it may have recentlyhad such sessions. As another example, the client 1290 may useinformation about other concurrent or past sessions with other servers.When the client 1290 sends a request to a server 1210 or a proxy 1212 toestablish a session, relevant information for that session is also madeavailable to one or both of the endpoints establishing the session.There are various ways in which the information may be made available tothe server. For example the information may be included with the requestitself. As another example, the server may request the information if itdoes not already hold the information in its communication information1215. As another example, the server may request the information from aremote or third party database, which has been populated withinformation from the client or from servers that have communicated withthe client. In any case, the communication session between the clientand the server is established using parameters that are determined atleast in part by the communication information available at the clientand/or server.

In some examples, the communication session may be established usinginitial values of packet pacing interval, congestion window,retransmission timeout and forward error correction. Initial valuessuitable for different types of networks (e.g. Wi-Fi, 4G), networkoperators and signal strength can be prespecified, and/or initial valuesfor successive connections can be derived from measured statistics ofearlier connections between the same endpoints in the same direction.For example:

The initial congestion window can be increased from its default value ifthe packet throughput of the previous connection is sufficiently largerthan the ratio of the default initial congestion window to the minimumround-trip time of the previous connection. The congestion window cansubsequently be adjusted downwards if the initial received acks from thenew connection indicate that the available rate has decreased comparedto the previous connection.

The initial pacing interval can be set e.g. as MAX(k1*congestionwindow/previous round-trip time, k2/previous packet throughput), wherek1 and k2 are configurable parameters, or, with receiver pacing, ask*previous pacing interval, where k increases with the loss rate of theprevious connection.

Forward error correction parameters such as code rate can be set ask*previous loss rate, where k is a configurable parameter. The initialretransmission timeout can be increased from its default value if theminimum round-trip time of the previous connection is larger.

Multi-Path

FIG. 166 shows the use of multiple paths between the server and clientto deliver the packet information. These multiple paths may be oversimilar or different network technologies with similar or differentaverage bandwidth, round trip delay, packet jitter rate, packet lossrate and cost. Examples of multiple paths include wired/fiber networks,geostationary, medium and low earth orbit satellites, WiFi, and cellularnetworks. In this example, the transmission control layer can utilize asingle session to distribute the N packets in the block beingtransmitted over the multiple paths according to a variety of metrics(average bandwidth of each path, round trip delay of each path, packetjitter rate, packet loss rate of each path, and cost). The N packets tobe transmitted in each block can be spread across each path in a mannerthat optimizes the overall end-to-end throughput and costs betweenserver and client. The number of packets sent on each path can bedynamically controlled such that the average relative proportions ofpackets sent on each path are in accordance with the average relativeavailable bandwidths of the paths, e.g. using back pressure-type controlwhereby packets are scheduled so as to approximately equalize queuelengths associated with the different paths.

For each path, the algorithms described above that embody transmissionand congestion control, forward error correction, sender based pacing,receiver based pacing, stream based parameter tuning, detection andcorrection for missing and out of order packets, use of informationacross multiple TCP connections, fast connection start and stop, TCP/UDPfallback, cascaded coding, recoding by intermediate nodes, and coding ofthe ACKs can be employed to improve the overall end-to-end throughputover the multiple paths between the source node and destination node.When losses are detected and FEC is used, the extra coded packets can besent over any or all of the paths. For instance, coded packets sent torepair losses can be sent preferentially over lower latency paths toreduce recovery delay. The destination node will decode any N of packetsthat are received over all of the paths and assemble them into a blockof N original packets by recreating any missing packets from the onesreceived. If less than N different coded packets are received across allpaths, then the destination node will request the number of missingpackets×where x=N−number of packets received be retransmitted. Any setof x different coded packet can be retransmitted over any path and thenused to reconstruct the missing packets in the block of N.

When there are networks with large differences in round trip time (RTT)latencies, the packets received over the lower RTT latencies will needto be buffered at the receiver in order to be combined with the higherRTT latency packets. The choice of packets sent on each path can becontrolled so as to reduce the extent of reordering and associatedbuffering on the receiver side, e.g. among the packets available to besent, earlier packets can be sent preferentially on higher latency pathsand later packets can be sent preferentially on lower latency paths.

Individual congestion control loops may be employed on each path toadapt to the available bandwidth and congestion on the path. Anadditional overall congestion control loop may be employed to controlthe total sending window or rate across all the paths of a multi-pathconnection, for fairness with single-path connections.

Referring to FIG. 167, a communication system utilizes a first,satellite data path 3102 having a relatively high round trip timelatency and a second, DSL data path 3104 having a relatively low roundtrip time latency. When a user application 3106 sends a request tostream video content, a content server 3108 (e.g., video streamingservice) provides some or all of the requested video content to a remoteproxy 3110 which generates encoded video content 3112 for transmissionto the user application 3106. Based on the RTT latencies of the firstdata path 3102 and the second data path 3104, the remote proxy 3110splits the encoded video content 3112 into an initial portion 3114(e.g., the first 5 seconds of video content) and a subsequent portion3116 (e.g., the remaining video content). The remote proxy 3110 thencauses transmission of the initial portion 3114 over the second, lowlatency data path 3104 and transmission of the subsequent portion 3116over the first, high latency data path 3102.

Referring to FIG. 168, due to the lower latency of the second data path3104, the initial portion 3114 of the video content arrives at the localproxy 3118 quickly, where it is decoded and sent to the user application3106 for presentation to a viewer. The subsequent portion 3116 of thevideo content is still traversing the first, high latency data path 3102at the time that presentation of the initial portion 3114 of the videocontent to the viewer commences.

Referring to FIG. 169, during presentation of the decoded initialportion 3114 of video content to the viewer, the subsequent portion 3116of the video content arrives at the local proxy 3118 where it is decodedand sent to the user application 3106 before presentation of the initialportion 3114 of the video content to the viewer is complete. In someexamples, sending the initial portion 3114 of the video content over thelow latency data path 3104 and sending a subsequent portion 3116 of thevideo content over the high latency data path 3102 avoids lengthy waittimes between when a user requests a video and when the user sees thevideo (as would be the case if using satellite only communication) whileminimizing data usage over the low latency data path (which may be morecostly to use).

In some examples, other types of messages may be preferentially sentover the low latency data path. For example, acknowledgement messages,retransmission messages, and/or other time critical messages may betransmitted over the low latency data path while other data messages aretransmitted over the higher latency data path.

In some examples, additional data paths with different characteristics(e.g., latencies) can also be included in the communication system, withmessages being balanced over any of a number of data paths based oncharacteristics of the messages (e.g., message type) and characteristicsof the data paths.

In some examples, other types of messages may be preferentially sentover the low latency data path. For example, acknowledgement messages,retransmission messages, and/or other time critical messages may betransmitted over the low latency data path while other data messages aretransmitted over the higher latency data path.

In some examples, additional data paths with different characteristics(e.g., latencies) can also be included in the communication system, withmessages being balanced over any of a number of data paths based oncharacteristics of the messages (e.g., message type) and characteristicsof the data paths.

Alternatives and Implementations

In the document above, certain features of the packet coding andtransmission control protocols are described individually, or inisolation, but it should be understood that there are certain advantagesthat may be gained by combining multiple features together. Preferredembodiments for the packet coding and transmission control protocolsdescribed may depend on whether the transmission links and network nodestraversed between communication session end-points belong to certainfiber or cellular carriers (e.g. AT&T, T-Mobile, Sprint, Verizon, Level3) and/or end-user Internet Service Providers (ISPs) (e.g. AT&T,Verizon, Comcast, Time Warner, Century Link, Charter, Cox) or are overcertain wired (e.g. DSL, cable, fiber-to-the-curb/home (FTTx)) orwireless (e.g. WiFi, cellular, satellite) links. In embodiments, probetransmissions may be used to characterize the types of network nodes andtransmission links communication signals are traversing and the packetcoding and transmission control protocol may be adjusted to achievecertain performance. In some embodiments, data transmissions may bemonitored to characterize the types of network nodes and transmissionlinks communication signals are traversing and the packet coding andtransmission control protocol may be adjusted to achieve certainperformance. In at least some embodiments, quantities such asround-trip-time (RTT), one-way transmission times (OWTT), congestionwindow, pacing rate, packet loss rate, number of overhead packets, andthe like may be monitored continuously, intermittently, in response to atrigger signal or event, and the like. In at least some embodiments,combinations of probe transmissions and data transmissions may be usedto characterize network and communication session performance in realtime.

In at least some embodiments, network and communication parameters maybe stored in the end-devices of communication sessions and/or they maybe stored in network resources such as servers, switches, nodes,computers, databases and the like. These network and communicationparameters may be used by the packet coding and transmission controlprotocol to determine initial parameter settings for the protocol toreduce the time it may take to adjust protocol parameters to achieveadequate performance. In embodiments, the network and communicationparameters may be tagged and/or associated with certain geographicallocations, network nodes, network paths, equipment types, carriernetworks, service providers, types of transmission paths and the like.In embodiments, the end-devices may be configured to automaticallyrecord and/or report protocol parameter settings and to associate thosesettings with certain locations determined using GPS-type locationidentification capabilities resident in those devices. In embodiments,the end-devices may be configured to automatically record and/or reportprotocol parameters settings and to associate those settings withcertain carrier networks, ISP equipment traversed, types of wired and/orwireless links and the like.

In at least some embodiments, a packet coding and transmission controlprotocol as described above may adjust more than one parameter toachieve adequate or improved network performance. Improved networkperformance may be characterized by less delay in delivering datapackets, less delay in completing file transfers, higher quality audioand video signal delivery, more efficient use of network resources, lesspower consumed by the end-users, more end-users supported by existinghardware resources and the like.

In at least some embodiments, certain modules or features of the packetcoding and transmission control protocol may be turned on or offdepending on the data's path through a network. In some embodiments, theorder in which certain features are implemented or controlled may beadjusted depending on the data's path through a network. In someembodiments, the probe transmissions and/or data transmissions may beused in open-loop or closed-loop control algorithms to adjust theadjustable parameters and/or the sequence of feature implementation inthe packet coding and transmission control protocol.

It should be understood that examples which involve monitoring tocontrol the protocol can in general involve aspects that are implementedat the source, the destination, or at a combination of the source andthe destination. Therefore, it should be evident that althoughembodiments are described above in which features are described as beingimplemented at particular endpoints, alternative embodiments involveimplementation of those features at different endpoints. Also, asdescribed above, monitoring to control the protocol can in generalinvolve aspects that are implemented intermediate nodes or points in thenetwork. Therefore, it should be evident that although embodiments aredescribed above in which features are described as being implemented atparticular endpoints, alternative embodiments involve implementation ofthose features at different nodes, including intermediate nodes,throughout the network.

In addition to the use of monitored parameters for control of theprotocols, the data may be used for other purposes. For example, thedata may support network analytics that are used, for example, tocontrol or provision the network as a whole.

The PC-TCP approaches may be adapted to enhance existing protocols andprocedures, and in particular protocols and procedures used in contentdelivery, for example, as used in coordinated content delivery networks.For instance, monitored parameters may be used to direct a client to theserver or servers that can deliver an entire unit of content as soon aspossible rather than merely direct the client to a least loaded serveror to server accessible over a least congested path. A difference insuch an new approach is that getting an entire file as fast as possiblemay require packets to be sent from multiple servers and/or servers thatare not geographically the closest, over multiple links, and using newacknowledgement protocols that coordinate the incoming data whilerequiring a minimum of retransmissions or FEC overhead. Coordinating mayinclude waiting for gaps in strings of packets (out-of-order packets) tobe filled in by later arriving packets and/or by coded packets. Inaddition, the PC-TCP approaches may improve the performance of wireless,cellular, and satellite links, significantly improving the end-to-endnetwork performance.

Some current systems use “adaptive bit rates” to try to preserve videotransmission through dynamic and/or poorly performing links. In someinstances, the PC-TCP approaches described above replace adaptive bitrate schemes and may be able to present a very high data rate to a userfor a long period of time. In other instances, the PC-TCP approaches areused in conjunction with currently-available adaptive bit rate schemesto support higher data rates on average than could be supported byadaptive bit rate schemes alone. In some instances, the PC-TCPapproaches may include integrated bit rate adjustments as part of itsfeature set and may use any and/or all of the previously identifiedadjustable parameters and/or monitored parameters to improve theperformance of a combined PC-TCP and bit-rate adaptive solution.

Certain embodiments described following relate to heating, and moreparticularly to cooking and recipes, including by use of intelligentdevices, and in a context of the IoT.

With the emergence of the IoT, opportunities exist for unlocking valuesurrounding a wide range of devices. To date, such opportunities havebeen limited for many users, particularly in rural areas of developingcountries, by the absence of robust energy and communicationsinfrastructure. The same problems with infrastructure also limit theability of users to access more basic features of certain devices; forexample, rather than using modern cooking systems, such as with gasburners, many rural users still cook over fires, using wood or otherbiofuel. A need exists for devices that meet basic needs, such as formodern cooking capability, without reliance on infrastructure, and anopportunity exists to expand the capabilities of basic cooking devicesto provide a much wider range of capabilities that will serve otherneeds and provide other benefits to users of cooking devices.

Many industrial environments are similarly isolated from conventionalenergy and communications infrastructure. For example, offshore drillingplatforms, industrial mining environments, pipeline operations,large-scale agricultural environments, marine exploration environments(e.g., deep ocean exploration), marine and other large-scaletransportation environments (such as ships, boats, submarines, aircraftand spacecraft) are often entirely isolated from the traditional powergrid, or require very expensive power transmission cables to receivepower from traditional sources. Other industrial environments areisolated for other reasons, such as to maintain “clean room” isolationduring semi-conductor manufacturing, pharmaceutical preparation, orhandling of hazardous materials, where interfaces like outlets andswitches for delivering conventional power potentially provide points ofpenetration or escape for contaminants or biologically active materials.For these environments, a need exists for cooking systems that provideimproved independence from conventional power sources. Also, in many ofthese environments fire is a significant hazard, among other thingsbecause of the presence of fire hazards and significant restrictions onegress for personnel. In those cases, storage of fuel for cooking in anenvironment presents a risk, because the fuel can exacerbate the extentof a fire, potentially resulting in disastrous consequences.Accordingly, such platforms and environments, such as oil drillingplatforms, may use diesel generators to power cooking and other systems,because diesel is perceived as presenting lower risk than propane,gasoline, or other fuel sources; however, diesel fuel also burns andremains a significant hazard. A need exists for safer mechanisms forproviding cooking capability in isolated industrial environments.

Intelligent cooking systems are disclosed herein, including anintelligent cooking system that is provided with processing,communications, and other information technology components, for remotemonitoring and control and various value-added features and services,embodiments of which use an electrolyzer, optionally a solar-poweredelectrolyzer, to produce hydrogen as an on-demand fuel stream for aheating element, such as a burner, of the cooking system.

Embodiments of cooking systems disclosed herein include ones forconsumer and commercial use, such as for cooking meals in homes and inrestaurants, which may include various embodiments of cooktops, stoves,toasters, ovens, grills and the like. Embodiments of cooking systemsalso include industrial cooking systems, such as for heating, drying,curing, and cooking not only food products and ingredients, but also awide variety of other products and components that are manufactured inand/or used in the industrial environments. These may include systemsand components used in assembly lines (such as for heating, drying,curing, or otherwise treating parts or materials at one stage ofproduction, such as to treat coatings, polymers, or the like that arecoated, dispersed, painted, or otherwise disposed on components), insemi-conductor manufacturing and preparation (such as for heating orcuring layers of a semi-conductor process, including in robotic assemblyprocesses), in tooling processes (such as for curing injection molds andother molds, tools, dies, or the like), in extrusion processes (such asfor curing, heating or otherwise treating results of extrusion), andmany others. These may also include systems and components used invarious industrial environments for servicing personnel, such as onships, submarines, offshore drilling platforms, and other marineplatforms, on large equipment, such as on mining or drilling equipment,cranes, or agricultural equipment, in energy production environments,such as oil, gas, hydro-power, wind power, solar power, and otherenvironments. Accordingly, while certain embodiments are disclosed forspecific environments, references to cooking systems should beunderstood to encompass any of these consumer, commercial and industrialsystems for cooking, heating, curing, and treating, except where contextindicates otherwise.

Provided herein is an intelligent cooking system leveraging hydrogentechnology plus cloud-based value-added-services derived from profiling,analytics, and the like. The smart hydrogen technology cooking systemprovides a standardized framework enabling other intelligent devices,such as smart-home devices and IoT devices to connect to the platform tofurther enrich the overall intelligence of contextual knowledge thatenables providing highly relevant value-added-services. The intelligentcooking system device (referred to herein in some cases as the“cooktop”), may be enabled with processing, communications, and otherinformation technology components and interfaces for enabling a varietyof features, benefits, and value added services including ones based onuser profiling, analytics, remote monitoring, remote processing andcontrol, and autonomous control. Interfaces that allowmachine-to-machine or user-to-machine communication with other devicesand the cloud (such as through application programming interfaces)enables the cooking system to contribute data that is valuable foranalytics (e.g., on users of the cooking system and on various consumer,commercial and industrial processes that involve the cooking system), aswell as for monitoring, control and operation of other devices andsystems. Through similar interfaces, the monitoring, control and/oroperation of the cooking system, and its various capabilities, canbenefit from or be based on data received from other devices (e.g., IoTdevices) and from other data sources, such as from the cloud. Forexample, the cooking system may track its usage, such as to determinewhen to send a signal for refueling the cooking system itself, to send asignal for re-supplying one or more ingredients, components or materials(such as based on detected patterns of usage of the same over timeperiods), to determine and provide guidance on usage of the cookingsystem (such as to suggest training or improvements in usage to improveefficiency or efficacy), and the like. These may include results basedon applying machine learning to the use of the fuel, the use of thecooking system, or the like.

In embodiments, the intelligent cooking system may be fueled by ahydrogen generator, referred to herein in some cases as theelectrolyzer, an independent fuel source that does not requiretraditional connections to the electrical power grid, to sources of gas(e.g., natural gas lines), or to periodic sources of supply ofconventional fuels (such as refueling oil, propane, diesel, or otherfuel tanks). The electrolyzer may operate on a water source to separatehydrogen and oxygen components and subsequently provide the hydrogencomponent as a source of fuel, such as an on-demand source of fuel, forthe intelligent cooking system. In embodiments, the electrolyzer may bepowered by a renewable energy source, such as a solar power source, awind power source, a hydropower source, or the like, thereby providingcomplete independence from the need for traditional powerinfrastructure. Methods and systems describing the design,manufacturing, assembly, deployment, and use of an electrolyzer areincluded herein. Among other benefits, the electrolyzer allows storageof water, rather than flammable materials like oil, propane, and diesel,as a source of energy for powering cooking systems in various isolatedor sensitive industrial environments, such as on or in ships,submarines, drilling platforms, mining environments, pipelineenvironments, exploration environments, agricultural environments, cleanroom environments, air- and space-craft environments, and others.Intelligent features of the cooking system can include control of theelectrolyzer, such as remote and/or autonomous control, such as toprovide a precise amount of hydrogen fuel (converted from water) at theexact point and time it is required. In embodiments, mechanisms may beprovided for capturing and returning products of the electrolyzer, suchas to return any unused hydrogen and oxygen into water form (ordirecting them for other use, such as using them as a source of oxygenfor breathing).

Methods and systems describing the design, manufacturing, assembly,deployment, and use of a smart hydrogen-based cooking system areincluded herein. Processing hardware and software modules for operatingvarious capabilities of the cooking system may be distributed, such ashaving modules or components that are located in sub-systems of thecooking system (e.g., the burners or other heating elements, temperaturecontrols, or the like), having modules or components located inproximity to a user interface for the cooking system (e.g., associatedwith a control panel), having modules or components located in proximityto a communications port for the cooking system (e.g., an integratedwireless access point, cellular communications chip, or the like, or adocking port for a communications devices, such as a smart phone),having modules or components located in nearby devices, such as othersmart devices (e.g., a NEST® thermostat), gateways, access points,beacons, or the like, and/or having modules or components located onservers, such as in the cloud or in a hosted remote control facility.

In embodiments, the cooking system may have a mobile docking facility,such as for docking a smart phone or other control device (such as aspecialized device used in an industrial process, such as aprocessor-enabled tool or piece of equipment), which may include powerfor charging the smart phone or other device, as well as datacommunications between the cooking system and the smart phone, such asto allow the smart phone to be used (such as via an app, browserfeature, or control feature of the phone) as a controller for thecooking system.

In embodiments, the cooking system may include various hardwarecomponents, which may include associated sensors for monitoringoperation, processing and data storage capabilities, and communicationcapabilities. The hardware components may include one or more burners orheating elements, (e.g., gas burners, electric burners, inductionburners, convection elements, grilling elements, radiative elements, andthe like), one or more fuel conduits, one or more level indicators forindicating fuel level, one or more safety detectors (such as gas leakdetectors, temperature sensors, smoke detectors, or the like). Inembodiments, a gas burner may include an on-demand gas-LPG hybridburner, which can burn conventional fuel like liquid propane, but whichcan also burn fuel generated on demand, such as hydrogen produced by theelectrolyzer. In embodiments, the burner may be a consumer cooktopburner having an appropriate power capability, such as being able toproduce 20,000 British Thermal Unit (“BTU”).

In embodiments, the cooking system may include a user interface thatfacilitates intuitive, contextual, intelligence-driven, and personalizedexperience, embodied in a dashboard, wizard, application interface(optionally including or integrating with one more associated smartphonetablet or browser-based applications or interfaces for one or more IoTdevices), control panel, touch screen display, or the like. The userinterface may include distributed components as described above forother software and hardware components. The application interface mayinclude interface elements appropriate for cooking foods (such arerecipes) or for using the cooking system for various consumer,commercial or industrial processes (such as recipes for makingsemi-conductor elements, for curing a coating or injection mold, andmany others).

Methods and systems describing the design, manufacturing, assembly,deployment and use of a solar-powered hydrogen production facility inconjunction with a hydrogen-based cooking system are included herein.

Methods and systems describing the design, manufacturing, assembly,deployment and use of a commercial hydrogen-based cooking system that issuitable for use in a range of restaurants, cafeterias, mobile kitchens,and the like are included herein.

Methods and systems describing the design, manufacturing, assembly,deployment and use of an industrial hydrogen-based cooking system thatis suitable for use as a food cooking system in various isolatedindustrial environments are included herein.

Methods and systems describing the design, manufacturing, assembly,deployment and use of an industrial hydrogen-based cooking system thatis suitable for use as a heating, drying, curing, treating or othercooking system in various industrial environments are included herein,such as for manufacturing and treating components and materials inindustrial production processes, including automated, robotic processesthat may include system elements that connect and coordinate with theintelligent cooking system, including in machine-to-machineconfigurations that enable application of machine learning to thesystem.

Methods and systems describing the design, manufacturing, assembly,deployment and use of a low-pressure hydrogen storage system aredescribed herein. The low-pressure hydrogen storage system may becombined with solar-powered hydrogen generation. In embodiments, thecooking system may receive fuel from the low-pressure hydrogen storagetank, which may safely store hydrogen produced by the electrolyzer. Inembodiments, the hydrogen may be used immediately upon completion ofelectrolyzing, such that no or almost no hydrogen fuel needs to bestored.

Methods and systems describing the architecture, design, andimplementation of a cloud-based platform for providingvalue-added-services derived from profiling, analytics, and the like inconjunction with a smart hydrogen-based cooking system are includedherein. The cloud-based platform may further provide communications,synchronization among smart-home devices and third parties, security ofelectronic transactions and data, and the like. In embodiments, thecooking system may comprise a connection to a smart home, including toone or more gateways, hubs, or the like, or to one or more IoT devices.The cooking system may itself comprise a hub or gateway for other IoTdevices, for home automation functions, commercial automation functions,industrial automation functions, or the like.

Methods and systems describing an intelligent user interface for acloud-based platform for providing value-added services (“VAS”) inconjunction with a smart hydrogen-based cooking system are includedherein. The intelligent user interface may comprise an electronic wizardthat may provide a contextual and intelligence driven personalizedexperience dashboard for computing devices that connect to a smart-homenetwork or a commercial or industrial network based around the smarthydrogen-based cooking system. The architecture, design andimplementation of the platform may be described herein.

Methods and systems for configuring, deploying, and providing valueadded services via a cloud-based platform that operates in conjunctionwith a smart hydrogen-based cooking system and a plurality ofinterconnected devices (e.g., mobile devices, Internet servers, and thelike) to prepare profiling, analytics, intelligence, and the like forthe VAS are described herein. In embodiments, the cooking system mayinclude various VAS, such as ones delivered by a cloud-based platformand/or other IoT devices. For example, among many possibilities, thecooking system may provide recipes, allow ordering of ingredients,components or materials, track usage of ingredients to prompt re-orders,allow feedback on recipes, provide recommendations for recipes(including based on other users, such as using collaborative filtering),provide guidance on operation, or the like. The architecture, design,and implementation of these methods and systems and of thevalue-added-services themselves may further be described herein.

In embodiments, through a user interface, such as a wizard, variousbenefits, features, and services may be enabled, such as various cookingsystem utilities (e.g., a liquid propane gas gauge utility, a cookingassistance utility, a detector utility (such as for leakage,overheating, or smoke, or the like), a remote control utility, or thelike). Services for shopping (e.g., a shopping cart or food orderingservice), for health (such as providing health indices for foods, andpersonalized suggestions and recommendations), for infotainment (such asplaying music, videos or podcasts while cooking), for nutrition (such asproviding personalized nutrition information, nutritional searchcapabilities, or the like) and shadow cooking (such as providing remotematerials on how to cook, as well as enabling broadcasting of the user,such as in a personalized cooking channel that is broadcast from thecooking system, or the like).

Methods and systems for profiling, analytics, and intelligence relatedto deployment, use, and service of a plurality of hydrogen-based cookingsystems that are deployed in a range of environments including urban,rural, commercial, and industrial settings are described herein. Urbansettings may include rural villages, low cost housing arrangements,apartments, housing projects, and the like where several end users(e.g., individual households, common kitchens, and the like) may bephysically proximal (e.g., apartments in a building, and the like). Thephysical proximity can facilitate shared access to platform components,such as a hydrolyser or low pressure stored hydrogen, and the like. Tothe extent that individual cooktop deployments may communicate throughlocal or Internet-based network access, additional benefits arise aroundtopics such as, planning for demand loading, and the like. An examplemay include generating and storing more hydrogen during the week whenpeople tend to cook a home than on the weekend, or using sharedinformation about recipes to facilitate bulk delivery of fresh items toan apartment building, multiple proximal restaurants, and the like. Inembodiments, the cooking system may enable and benefit from analytics,such as for profiling, recording or analyzing users, usage of thedevice, maintenance and repair histories, patterns relating to problemsor faults, energy usage patterns, cooking patterns, and the like.

These methods and systems may further perform profiling, analytics, andintelligence related to deployment, use and service of solar-poweredelectrolyzers that generate hydrogen that is stored in a low-pressurehydrogen storage system.

Methods and systems related to extending the capabilities and access tocontent and/or VAS of a smart hydrogen-based cooking system throughintelligent networking and development of transaction channels aredescribed herein.

Methods and systems of an ecosystem based around the methods and systemsof generating hydrogen via solar-powered electrolyzers, storing thegenerated hydrogen in low pressure storage systems, distribution and useof the stored hydrogen by one or more individuals, and the like, aredescribed herein. In embodiments, the cooking system, or a collection ofcooking systems, may provide information to a broader businessecosystem, such as informing suppliers of foods or other materials orcomponents of aggregate information about usage, informing advertisers,managers and manufacturers about consumption patterns, and the like.Accordingly, the cooking system may comprise a component of a businessecosystem that includes various parties that provide variouscommodities, information, and devices.

Another embodiment of smart cooking technology described herein mayinclude an intelligent, computerized knob or dial suitable for directuse with any of the cooking systems, probes, single burner and otherheating elements, and the like described herein. Such a smart knob ordial may include all electronics and power necessary for independentoperation and control of the smart systems described herein.

In embodiments, the cooking system is an industrial cooking system usedto provide heat in a manufacturing process. In embodiments, theindustrial cooking system is used in at least one of a semi-conductormanufacturing process, a coating process, a molding process, a toolingprocess, an extrusion process, a pharmaceutical manufacturing processand an industrial food manufacturing process.

In embodiments, a smart knob is adapted to store instructions for aplurality of different cooking systems. In embodiments, a smart knob isconfigured to initiate a handshake with a cooking system based on whichthe knob automatically determines which instructions should be used tocontrol the cooking system. In embodiments, a smart knob is configuredwith a machine learning facility that is configured to improve thecontrol of the cooking system by the smart knob over a period of usebased on feedback from at least one user of the cooking system.

In embodiments, a smart knob is configured to initiate a handshake witha cooking system to access at least one value-added service based on aprofile of a user.

DETAILED DESCRIPTION

Referring to FIG. 170, an integrated cooktop embodiment 11 of theintelligent cooking system methods and systems 21 described herein isdepicted. The cooktop embodiment 11 of FIG. 170, may include one or moreburners 31 that may burn one or more types of fuel, such as LiquidPropane Gas (LPG), hydrogen, a combination thereof, and the like. Gasburners may, for example, be rated to provide variable heat, includingup to a maximum heat, thereby consuming a corresponding amount of fuel.One or more of the burners 31 may operate with an LPG source 51 and asource of hydrogen gas 61 such that the hydrogen source 61 may beutilized based on a demand for fuel indicated by the burner 31, ameasure of available LPG fuel, an amount of LPG fuel used over time, andany combination of use, demand, historical usage, anticipated usage,availability of supply, weather conditions, calendar date/time (e.g.,time of day, week, month, year, and the like), proximity to an event(e.g., an intense cooking time, such as just before a holiday), and thelike. The hydrogen source 61 may be utilized so that the amount of otherfuel used, such as LPG, is kept below a usage threshold. Such a usagethreshold may be based on costs of LPG gas, uses of LPG gas by otherburners 31 in the cooking system 21, other cooking systems 21 in thevicinity (e.g., other cooking systems 21 in a restaurant, other cookingsystems 21 in nearby residences), and the like. Each cooking system 21and/or burner 31 within the cooking system 21 may therefore provideon-demand fuel sourcing dynamically without need for user input ormonitoring of the cooking system 21. By automating fuel sourcing, theburner may extend the life of available LPG by automatically introducingthe hydrogen fuel, such as by switching from one source to the other orby reducing one source (e.g., LPG) while increasing the other (e.g.,hydrogen). The degree to which each fuel source is utilized may be basedon a set of operational rules that target efficiency, LPG fuelconsumption, availability of hydrogen, and the like. Rating of the oneor more burners 31 may be under the control of a processor, including toprovide different levels of rating for different fuel sources, such asLPG alone, hydrogen alone, or a mixture of LPG and hydrogen with a givenratio of constituent parts.

Each of the burners 31, cooking systems 21, or collection of cookingsystems 21 may be configured with fuel controls, such as fuel mixingdevices (e.g., valves, shunts, mixing chambers, pressure compensationbaffles, check valves, and the like) that may be controlledautomatically based, at least in part on some measure of historical,current, planned, and/or anticipated consumption, availability, and thelike. In an example, one or more burners 31 may be set to produce 1000 Wof heat and a burner gas source control facility may activate one ormore gas mixing devices while monitoring burner output to ensure thatthe burner output does not deviate from the output setting by more thana predefined tolerance, such as 100 W or ten percent (10%).Alternatively, a model of gas consumption and burner output, embodied ina software module that may have access to data sources regarding typesof gas, burning characteristics, types of burners, ratingcharacteristics, and the like, may be used by the control facility toregulate the flow of one or more of gasses being mixed to deliver aconsistent burner heat output. Any combination of burner output sensing,modeling, and preset mixing control may be used by the control facilitywhen operating fuel sourcing and/or mixing devices.

The one or more burners 31 may include intelligence for enhancingoperation, efficiency, fuel conservation, and the like. Each of theburners 31 may have its own control facility 101. A centralized cookingsystem control facility may be configured to manage operation of theburners 31 of the cooking system 21 or other heating elements notedthroughout this disclosure. Alternatively, the individual burner controlfacilities 101 may communicate over a wired and/or wireless interface tofacilitate combined cooking system burner control. One or more sensorsto detect presence of an object in the targeted heating zone (e.g.,disposed on the burner grate) may provide feedback to the controlfacility. Object presence sensors may also provide an indication of thetype, size, density, material, and other aspect of the detected objectin the targeted heating zone. Detection of a material such as metal,versus cloth (e.g., a person's sleeve), versus human flesh mayfacilitate efficiency and safety. When cloth or human flesh is detected,the control facility may inhibit heat production so as to avoid burningthe person's skin or causing their clothing to catch fire. Such acontrol facility safety feature may be over ridden through user input tothe control facility to give the user an opportunity to determine if theinhibited operation is proper. Other detectors, such as spill over(e.g., moisture) detectors in proximity to the burner may help inmanaging safety and operation. A large amount of spillage from a pot maycause the flame being produced by the burner to be extinguished. Basedon operational rules, the source of gas may be disabled and/or anigniter may be activated to resume proper operation of the burner. Otheractions may also be configured into the control facility, such assignaling the condition to a user (e.g., through an indicator on thecooking system 21, via connection to a personal mobile device, to acentral fire control facility, and the like).

Burner control facilities 101 may control burner heat output (andthereby control fuel consumption) based on one or more models ofoperation, such as to heat a pan, pot, component, material, or otheritem placed in proximity to the burner 21 or other heating element. Asan example, if a user wants to boil water in a heavy metal pot quickly,a control facility may cause a burner to produce maximum heat. Based onuser preferences and/or other factors as noted above related to demand,supply, and the like, the control facility may adjust the burner outputwhile notifying the user of a target time for completion of a heatingactivity (e.g., time until the water in the pot boils). In this way anintelligent burner 21 (e.g., on with a burner control facility) mayachieve some user preferences (e.g., heating temperature) whilecompromising on others (e.g., amount of time to boiling, and the like).The parameters (e.g., operational rules) for such tradeoff may beconfigured into the cooking system 21/burner 31 during production, maybe adjustable by the user directly or remotely, may be responsive tochanging conditions, and the like. In embodiments, machine learning,either embodied at the cooking system 21, in the cloud, or in acombination, may be used to optimize the parameters for given objectivessought by users, such as cooking time, quality of the result (e.g.,based on feedback measures about the output product, such as taste inthe case of foods or other quality metrics in the case of other productsof materials). For example, the cooking system 21 may be configuredunder control of machine learning to try different heating patterns fora food and to solicit user input as to the quality of the resultingitem, so that over time an optimal heating pattern is developed.

The intelligent cooking system 21 as described herein and depicted inFIG. 170 may include an interface port 127 with supporting structuralelements to securely hold a personal mobile device 150 (e.g., a mobilephone) in a safe and readily viewable position so that the user may haveboth visual and at least auditory access to the device. The cookingsystem 21 may include features that further ensure that the mountedmobile device 150 is not subject to excessive heat, such as heatshields, deflectors, air flow baffles, heat sinks, and the like. Asource of airflow may be incorporated to facilitate moving at least aportion of heated air from one or more of the burners 31 away from amounted personal mobile device 150.

The intelligent burner embodiment 280 depicted in FIG. 171 represents asingle burner embodiment 210 of the intelligent cooking system 21described herein. Any, none, or all features of a multi-burnerintelligent cooking system 21 may be configured with the single burnerversion depicted in FIG. 171. Further depicted in FIG. 171 is a versionof the intelligent burner 280 that may have an enclosed burner chamber220 that provides heat in a target heat-zone as a plane of heat ratherthan as a volume of heat. This may be generated by induction,electricity, or the like that may be produced by converting a source offuel, such as LPG and/or hydrogen with a device that may produceelectricity from a combustible gas.

The intelligent cooking system 21 may be combined with a hydrogengenerator 300 to provide a source of hydrogen for use with the burners31 as described herein. FIG. 172 depicts a solar-powered hydrogenproduction and storage station 320. The hydrogen production station 320may be configured with one or more solar collectors 330, such assunlight-to-electricity conversion panels 340 that may produce energyfor operating an electrolyzer 350 that converts a hydrogen source, suchas water vapor, to at least hydrogen and oxygen for storage. Energy fromthe solar collectors 330 may power one or more electrolyzers 350, suchas one depicted in the embodiment 700 of FIG. 176. The one or moreelectrolyzers 350 may process water vapor, such as may be available inambient air, for storage in a storage system 360, such as a low-pressurestorage system 370 depicted in FIG. 172. Alternatively, and/or inaddition to processing air-born water vapor, a source of water, such ascollected rainfall, public water supply, or other source may beprocessed by the electrolyzer 350 to produce hydrogen fuel.

As hydrogen fuel is produced, it may be stored in a suitable storagecontainer, such as the low-pressure storage system 370 that may beconfigured with the solar-powered electrolyzer system 350. The hydrogenproduced by the solar-powered electrolyzer 350 may be routed to one ormore intelligent cooking systems 21 in addition to or in place of beingrouted to a storage system 360. A hydrogen production and storage system320 may produce hydrogen based on a variety of conditions including,without limitation, availability of a source of water vapor,availability of power to the electrolyzer, an amount of sunlight beingcollected, a forecast of sunlight, a demand for hydrogen energy, aprediction of demand, based on availability of LPG, usage of LPG, andthe like.

The low-pressure gas storage system 370 may store the hydrogen andoxygen in ultraviolet (“UV”) coated plastic bags or through waterimmersion technology (e.g., biogas). The maximum pressure inside thesystem may be less than 1.1 bar, which promotes safety, as the pressureis very low. Also, as no compressors are used, the cost for storage ismuch lower than for active storage systems that store compressed gas.FIG. 173, FIG. 174, and FIG. 175 depict an embodiment 400 of such alow-pressure storage system 370, with an inlet valve 411 and outletvalve 413 providing ports into an interior storage area 415 with theinternal volume separated into two parts.

The low pressure set up may directly work from renewable energy, such assolar energy collected by solar cells, wind energy, hydro-power, or thelike, improving the efficiency. The selected source of renewable energymay be based on characteristics of the environment; for example, marineindustrial environments may have available wind and hydro-power,agricultural environments may have solar power, etc. Also if therenewable energy (e.g. solar energy) collection facility is connected toa power grid, the electricity generated and the energy stored may beprovided to the grid, e.g., during high cost periods. Likewise, the gridmay be used to restore any used energy during off peak hours at reducedcosts.

The designed low-pressure storage may be used to store hydrogen, as asource of energy, that may be converted into electricity. The designedsystem may store energy at very low cost and may have a lifetime ofyears, e.g., more than 15 years, which modern batteries don't have.Amounts of storage may be configured to satisfy safety requirements,such as storing little enough to cause a minimal fire hazard as comparedto storing larger amounts of other fuels.

In an embodiment, the intelligent cooking system 21 may signal to theelectrolyzer system 350 a demand for hydrogen fuel. In response, theelectrolyzer system 350 may direct stored hydrogen to the cooking system21, begin to produce hydrogen, or indicate that hydrogen is notcurrently available. This response may be based, at least in part onconditions for producing hydrogen. If conditions for producing hydrogenare good, the electrolyzer system may begin to produce hydrogen fuelrather than merely sourcing it from storage. In this way, thecontemporaneous demand for hydrogen fuel and an ability to produce itmay be combined to determine the operation of the energy production andconsumption systems.

The intelligent cooking system 21 and/or hydrogen production and storagesystems described herein may be combined with a platform that interactswith electronic devices and participants in a related ecosystem ofsuppliers, content providers, service providers, regulators, and thelike to deliver VAS to users of the intelligent cooking system 21, usersof the hydrogen production system, and other participants in theecosystem. Certain features of such a platform 800 may be depicted inFIG. 177. The platform 800, which may be a cloud-based platform, mayhandle cooking system utilities, such as leakage sensing, fuel sourcing,usage assistance, remote control, and the like. In an example, a userwho is located remotely from the intelligent cooking system 21 mayconfigure the cooking system 21 to operate at a preset time, or based ona preset condition from his/her computing device (e.g., a personalmobile phone, desktop computer, laptop, tablet, and the like). The usermay further be notified when the cooking system 21 begins to operate,thereby ensuring the user that the cooking system 21 is operating asexpected. A user or third party (e.g., a regulatory agency, landlord,and the like) may configure one or more present conditions. Suchconditions may include a variety of triggers including time, location ofa user or third party, and the like. In an example, a parent may want tohave a cooking system operate to warm up ingredients based on ananticipated arrival of someone to the home. This anticipation may bebased on a detected location of a mobile device being carried by aperson whose arrival is being anticipated.

The platform 800 may further connect cooking system users withparticipants in the ecosystem (e.g., vendors and/or service providers)synergistically so that both the user and the participants may benefitfrom the platform 800. In an example, a user may plan to prepare a mealfor an upcoming dinner. The user may provide the meal plan to theplatform 800 (e.g., directly through the user's mobile phone, via theuser's intelligent cooking system 21, and the like). The platform 800may determine that fresh produce for the meal is preferred by the userand may signal to retailers and/or wholesalers to have the produceavailable for the user to pick up on his/her return to the home toprepare the meal. In this way, vendors and service providers whoparticipate in the ecosystem may gain insight into their customer'sneeds. Likewise, users may indicate a preference for a type of meal thatmay be prepared with a variety of proteins. Participants in theecosystem may make offers to the user to have one or more of the typesof protein available for the user on the day and at the time preferredby the user. A butcher that is located in proximity to the user's returnpath may offer conveniences, such as preparation of cuts of meat for theuser. Butchers who may not be conveniently located in proximity to theuser's return path may offer delivery services on a day and time thatbest complies with the user's meal plans.

A user of such a platform-connected intelligent cooking system mayleverage the platform 800 to gain both access to and analysis ofinformation that is available across the Internet to address particularuser interests, such as health, nutrition, and the like. As an example,a user may receive guidance from a health professional to reduce redmeat intake and increase his seafood intake. The platform 800 mayinteract with the user, the cooking system, and ecosystem participantsto facilitate preparing variations of a family's favorite meals withfish instead of red meat. Changes in spices, amounts, cooking times,recipes, and the like may be provided to the user and to the cookingsystem 21 through the platform 800 to make meal preparation moreenjoyable. The platform 800 may help with nutritional assistance, suchas by providing access to quality nutritional professionals who may workpersonally with a user in meal selection and preparation.

The platform 800 may also help a user of the platform 800, even one whodoes not have access to the intelligent cooking system 21, to benefitfrom the knowledge gathering and analysis possible from a platform 800interconnected with a plurality of cooking systems, users, and ecosystemparticipants. In an example, the platform 800 may provide guidance to auser in the selection and purchase of an intelligent burner and/orintegrated cooking system and related appliances (e.g., refrigeration),utensils, cookware, and the like.

The platform 800 may further facilitate integration with VAS, such asfinancial services (e.g., for financing infrastructure and operatingcosts), healthcare services (e.g., facilitating connecting healthcareproviders with patients at home), smart home solutions (e.g., thosedescribed herein), rural solutions (e.g., access to products andservices by users in rural jurisdictions), and the like. Information(e.g., profiles, analytics, and the like) gathered and/or generated bythe platform 800 may be used for other business services either directlywith or through other partners (e.g., credit rating agencies fordeveloping markets).

The platform 800 may facilitate a range of user benefits, includingshopping, infotainment, business development, and the like. In abusiness development example, a user may utilize her intelligentintegrated cooking system 21 to produce her own cooking show by settingup her personal phone with camera on the cooking system 21 so that theuser activity on the cooking system 21 may be captured and/ordistributed to other users via the platform 800. Further in the example,a user may schedule a cooking demonstration and may allow other users tocook along with him in an autonomous and/or interactive way. A user mayopt into viewing and cooking along with the cooking show producerwithout directly interacting with the producer. Whereas, another usermay configure his cooking system 21 with a personal mobile device andallow others to provide feedback based on the user's activities on thecooking system 21 via the camera and user interface of the mobiledevice.

The platform 800 may facilitate establishing an IoT ecosystem of smarthome devices, such as, in embodiments, a smart kitchen that enables andempowers the homemaker. The smart kitchen may include a smart cookingsystem 21, IoT middleware and a smart kitchen application. The smartcooking system 21 may provide a hardware layer of the platform 800 thatmay provide plug and play support for IoT devices, with each new deviceacting as a node providing more information, such as from additionalsensors, to the entire system. IoT cloud support, which may beconsidered as a middleware layer of the platform 800, may enable thecommunication (such as by streaming) and storage of data on the cloud,along with enabling optional remote management of various capabilitiesthe platform 800. A smart kitchen application may include a userinterface layer that may provide a single point of access and controlfor the entire range of smart devices for the ease of the homemaker orother user. As an example of a smart kitchen enabled by the smartcooktop methods and systems described herein, an exhaust fan may beturned on as the water in a pot begins to boil, thereby directing thesteam output of the pot away from the kitchen. This may be done througha combination of sensors (e.g., a humidity sensor), automated cookingsystem control that determines when the pot will begin to boil based onthe weight of the pot on the burner, and the energy level of the burner,and the like Similar embodiments may be used in industrial environments,such as coordination with ventilation systems to maintain appropriatetemperature, pressure, and humidity conditions by coordination ofheating activities via the cooking system 21 and routing and circulationof air and other fluids by the ventilation system. The cooking systemcontroller may, for example, communicate with an exhaust fan controllerto turn on the fan based on these inputs and/or calculations; therebyimproving the operation of the smart kitchen appliances while conservingenergy through timely application of the exhaust fan. A flow chartrepresentative of operational steps 5600 for this example is depicted inFIG. 225.

The value created by such a platform 800 may be broadly classified into(i) VAS; (ii) profiling, learning and analytics; and (iii) a smart homesolution or IoT solution for a commercial or industrial environment. TheVAS of the system, may include without limitation: (a) personalizednutrition; (b) information and entertainment (also referred to as“infotainment”); (c) family health; (d) finance and commerce services(including online ordering and shopping); (e) hardware control services;and many other types of services.

Profiling, learning and analytics may provide a number of benefits tovarious entities. For example, a homemaker may get access topersonalized nutrition and fitness recommendations to improve the healthof the entire family, including healthy recipe and diet recommendations,nutritional supplement recommendations, workout and fitnessrecommendations, energy usage optimization advice for cooking and use ofother home appliances, and the like. Device manufacturers and otherenterprises may also benefit, as the platform 800 may solve the problemsfaced by home appliance device manufacturers in integrating theirdevices to the cloud and leveraging the conveniences provided by thesame. Device manufacturers and other enterprises may be provided with aninterface to the platform 800 (such as by one or more applicationprogramming interfaces, graphical user interfaces, or other interfaces)that may enable them to leverage capabilities of the platform 800,including, one or more machine learning algorithms or other analyticcapabilities that may learn and develop insights from data generated bythe device. These capabilities may include an analytics dashboard fordevices; a machine learning plug and play interface for developing datainsights; a health status check for connected appliances (e.g., to knowwhen a device turns faulty, such as to facilitate quick and easyreplacement/servicing); and user profiling capabilities, such as tofacilitate providing recommendations to users, such as based oncollaborative filtering to group users with other similar users in orderto provide targeted advice, offers, advertisements, and the like.

A smart home solution or IoT solution for a commercial or industrialenvironment may provide benefits to device manufacturers who find itdifficult to embed complex electronics in their devices to make themintelligent due to development and cost constraints. The platform 800simplifies this by providing a communication layer that may be used bypartners to send their device data, after which the platform 800 maytake over and provide meaningful data and insights by analyzing the dataand performs specific actions on behalf of an integrated smart home forthe user. Additional value of each partner interacting through theplatform 800 is the access to various sensory data built into the systemfor effectively making any connected device more intelligent. Forexample, among many possibilities, the ambient temperature sensor insidethe smart cooking system 21 may be leveraged by a controllable exhaustfacility to accordingly increase the airflow for the comfort of thehomemaker.

Referring to the smart home embodiment of FIG. 178, an intelligentcooking system 900 may be a participant in or may be a gateway to a homeappliance network that may include other kitchen appliances, sensors,monitors, user interface devices, processing devices, and the like. Thehome appliance network, and/or the devices configured in the homenetwork, may be connected to each other and to other participants of theecosystem through the platform 800 (FIG. 177). Data collected from theseappliances, participants in the ecosystem, users of the platform, thirdparties, and the like may provide an interactive environment to explore,visualize, and study patterns, such as fuel usage patterns. Datacollected may further be synthesized through deep machine learning,pattern recognition, modeling, and prediction analysis to providevaluable insights related to all aspects of the platform participants,devices, suppliers, and the larger ecosystem.

Further embodiments of the hydrogen generation and consumptioncapabilities are now described.

The system may use water and electricity as fuel to generate thegas-on-demand that may be used, for example, for cooking. The hydrogenand oxygen generated in the cell may be separated out within the celland kept separate until reaching the combustion port in a burner. Aspecially designed burner module may comprise different chambers toallow passage of hydrogen, oxygen, and cooking gas. The ports forhydrogen and cooking gas may be designed in such a way as to avoid flameflashbacks and flame lift-offs, and the like. The oxygen ports may bedesigned to ensure optimum supply of oxygen with respect to the hydrogensupply. The hydrogen and oxygen ports may be on mutually perpendicularplanes ensuring proper intermixing of the burning mixture. The hydrogenand cooking gas connections may be mutually independent and may beoperated separately or together to generate a mixed flame.

A hydrogen production and use system 1000 as disclosed herein maycomprise one or more of the following elements as depicted in FIGS. 179and 180. An electrolytic cell 1101 is detailed in FIG. 180, which showsan exploded view of the cell consisting of steel electrodes separated bynylon membranes inside polyvinyl chloride (“PVC”) gaskets sandwiched byacrylic sheets. The cell may comprise an alkaline electrolytic cell thatseparates water into its constituent components of hydrogen and oxygen.A mixture tank, such as a concentrated alkaline mixture tank may serveas the electrolyte source for the electrolytic cell. The alkali mixturemay be prepared by mixing a base like potassium hydroxide (“KOH”) orsodium hydroxide (“NaOH”) with water. In case of KOH, in embodiments theconcentration may be around 20%. The membrane for separation of gaseswithin the cell may be made from a variety of materials. One suchmaterial is a nylon sheet with catalyst coating that has enough threadcount to allow ion transfer and minimal gas transfer. The electrodesused may be, for example, stainless steel or nickel coated stainlesssteel. Also provided may be gas bubbling tanks. The hydrogen and oxygengenerated from the electrolytic cells may be passed through gas bubblingtanks. The tanks may be made with recirculation or non-recirculationmodes. In a non-recirculation mode, the gas is bubbled through water andany impurity in the gas gets purified in the process. In recirculationmode, the gas is bubbled through KOH solution, which may be identical inconcentration to the alkaline mixture tank. In this methodology, anyadditional electrolyte that flows out with the gas gets re-circulatedinto the alkaline mixture tank. The two bubbling tanks may be connectedtogether, such as at the bottom, to ensure pressure maintenance acrossthem. Dehumidifiers may also be included. The gas passed through thebubblers may have excess moisture content that reduces the combustionefficiency. Hence, the gas may be passed through dehumidifiers, whichmay use a desicmayt, water-gas separator membranes, or otherdehumidification technologies, or a combination thereof, to reduce thehumidity content of the gas. A hydrogen burner arrangement is providedwherein a conventional hydrogen burner, as known in the art, may beconnected to the dehumidifier, such as through a flashback arrestor. Inembodiments, there are no ports for air intake, as combustion of thehydrogen-air mixture may result in an elevated concentration ofmono-nitrogen oxides (“NOx”), which in turn may result in a potentialfor flame flashback. The burner ports may have a small diameter, such aslower than 0.5 mm, to reduce the chance of any flame flashback. Theports may be aligned in such a way as to cross-ignite, resulting incombustion of the complete gas supply with a single spark. The hydrogenconcentration throughout the supply line may be above the maximumcombustion limit, and hence there is little safety hazard. The oxygensupply may be through a channel that is completely separate from thehydrogen one. The oxygen ports may be located on a plane perpendicularto the hydrogen ports to ensure proper mixing of the combustion mixture.Above the burner, a catalyst may be placed so as to lower thetemperature of combustion, reducing the concentration of NOx generated.An economically feasible high temperature catalyst mesh may be used tolower the temperatures of combustion.

The power supply may supply a desired voltage that may be optimizedaccording to the conditions of the system, such as the watertemperature, pressure, etc. The voltage per cell may vary, such as from1.4 v to 2.3 v, and the current density may be as low as 44 mA/cm² formaximum efficiency. As the current density is low, the efficiency tendsto be high.

An LPG/cooking gas burner arrangement may be provided. The LPG/cookinggas burner arrangement may be added to the hydrogen burner arrangement.In embodiments, the system may be similar to a closed top burnerarrangement, where the burner ports are along the sides of the burnerand the flame fueled by the LPG surrounds the hydrogen flame. Inembodiments, the gas supply channel may be kept separate from thehydrogen supply channel and the oxygen supply channel and would hencepose no safety risk in that regard. In alternative embodiments, thefuels may be mixed, such as under control of a processor.

A renewable energy connection may be provided. In embodiments, the wholesystem, including the storage system, may be connected to renewableenergy sources like solar power, wind power, water power, or the like.The hydrogen storage may act as storage for energy produced by such arenewable energy source.

In yet another embodiment of the system, the actuation of the combustionmay be done using a sensor placed along the oxygen supply channel todetect the presence of a cooking utensil on the burner. The sensor maybe shielded from the heat and made to work at an optimum temperature.

In yet another embodiment of the system, the hydrogen flame may be usedto heat a coil that could hence radiate heat for more spread outcooking. The hydrogen supply to the radiator may be regulated by thetemperature within the radiator.

In yet another embodiment of the system, the heat absorbed by thecatalyst mesh may be used to generate electric power, increasing the netefficiency of the system.

The hydrogen production system may be integrated into a cooking system1201 as depicted in FIG. 181, which may include smart cooking systemcomprising a microcontroller with basic sensors, such as gyro,accelerometer, temperature and humidity. Other sensors like weight,additional temperature sensors, pressure sensors, and the like may bemounted on the cooking system and, based upon various inputs from theuser and the system (including optional remote control), the actuatorsmay control the cooking temperature, time and other cooking functions.

A speaker may sometimes be used to read out the output or simply playmusic.

The microcontroller may also be interfaced with a display and touchinterface.

The microcontroller may be connected with the cloud, where informationregarding recipes, weight and temperature, and the like may be storedand accessed by the controller. The microcontroller may also provideinformation on the user's cooking patterns.

In an embodiment, smart system configuration, control, and cookingalgorithms may be executed by computers (e.g., in the cloud) to processall gathered and sensed information, optionally providing arecommendation related to the operation to the end user. Therecommendation may include suggesting suitable recipes, auto turning ofthe heat in the burner, and the like. The microcontroller maycommunicate via Bluetooth low energy (“BLE”), Wi-Fi and/or lowaran, orthe like, such as to ensure connectivity to the cloud. Lowaran is awireless network that leverages long-range radio signals forcommunicating between IoT devices and cloud devices via a centralserver. The microcontroller may be designed in such a way that it hasenough processing power to connect to other IoT devices that may havelittle or no processing power and also do processing for these IoTdevices to give the end user a smart and intelligent, all in one, smarthome solution.

FIG. 182 and FIG. 183 depict auto-switching connectivity 1301 in theform of ad hoc Wi-Fi from a cooktop 1310 through nearby mobile devices1371 may be performed in the event of non-availability of a common homeWi-Fi router 1340 to ensure cloud connectivity 1360 whenever possible.FIG. 182 depicts a normal connectivity mode when Wi-Fi 1340 isavailable. FIG. 183 depicts ad hoc use of local mobile devices 1400 forconnectivity to the cloud 1360.

Additional smart cooking system features and capabilities may includeweight sensors for each heating element that, when combined with cookinglearning algorithms, may control fuel consumption to minimizeovercooking and waste of fuel. This may also benefit configurations thatemploy multiple heating elements, so that unused heating elements do notcontinue to operate and waste fuel. FIG. 184 depicts a three-elementinduction smart cooking system 1500. Heating elements may be gas-basedor may alternatively include heating with induction, electric hot plate,electric coil, halogen lamp, and the like. FIG. 185 depicts a singleburner gas smart cooking system 1600. FIG. 186 depicts an electric hotplate (coil) smart cooking system 1700. FIG. 187 depicts a singleinduction heating element smart cooking system 1800.

Another embodiment of smart cooking technology described herein may bean intelligent, computerized knob, dial, slider, or the like suitablefor direct use with any of the cooktops, probes, single burner elements,and the like described herein. Such a smart knob 2000 may include allelectronics and power necessary for independent operation and control ofthe smart systems described herein. References to a smart knob 2000should be understood to encompass knobs, dials, sliders, toggles andother physical user interface form factors that are conventionally usedto control temperature, timing and other factors involved in heating,cooking, and the like, where any of the foregoing are embodied with aprocessor and one or more other intelligent features.

The smart knob 2000 may include an embodiment with a digital actuator,such as for electric-based cooking systems and another embodiment with amechanical actuator, such as for gas models. The smart knob 2000 may bedesigned with portability and functionality in mind. The knob mayinclude a user interface (e.g., display, audio output, and the like)through which it may provide users step-by-step recipes, and the like.The smart knob 2000 may operate wirelessly, so that it may set alarmsand also monitor the operation of a plurality of smart cooking systems21 even if it is removed from the cooking system actuator. The smartknob 2000 may, in embodiments, store information that allows it tointerface with different kinds of cooking systems, such as by includingprograms and instructions for forming a handshake (e.g., by Bluetooth™or the like) with a cooking system to determine what control protocolshould be used for the cooking system, such as one that may be managedremotely, such as in a cloud or other distributed computing platform. Inembodiments, a user may bring the smart knob 2000 in proximity to thecooking system 21, in which case a handshake may be initiated (eitherunder user control or automatically), such that the smart knob 2000 mayrecognize the cooking system 21 and either initiate control based onstored instructions on the knob 2000 or initiate a download ofappropriate programming and control instructions for the cooking system21 from a remote source, such as a cloud or other distributed computingplatform to which the knob 2000 is connected. Thus, the knob 2000 servesas a universal remote controller for a variety of cooking systems, wherea user may initiate control using familiar motions, such as turning adial to set a timer or temperature setting, moving a toggle or slider upor down, setting a timer, or the like. In embodiments, a plurality ofknobs 2000 may be provided that coordinate with each other to control asingle burner or heating element or a collection of burners or heatingelements. For example, one of the knobs 2000 in a pair of knobs mightcontrol temperature of a burner or heating element, while a second knobin the pair might control timing for the heating.

In embodiments, the smart knob 2000 may be used to embody complexprotocols, such as patterns of temperatures over time, such as suitablefor heating an item to different temperatures over time. These may bestored as recipes, or the like, so that a user may simply indicate, viathe knob 2000, the desired recipe, and the knob 2000 will automaticallyinitiate control of a burner or heating element to follow the recipe.

A user may use the smart knob 2000 with an induction cooking system forcontrolling the temperature of a cooking system, such as an inductionstove, providing step-by-step instructions, and the like. The user may,for example, switch to cooking with a gas burner-based smart cookingsystem by simply taking the smart knob 2000 off of the induction cookingsystem, configuring it to operate the gas burner cooking system (such asby initiating an automated handshake), and mounting the knob 2000 in aconvenient place, such as countertop, wall, refrigerator door, and thelike. It should be noted that while the knob 2000 may be placed on thecooking system, once a connection has been established, such as byBluetooth™, near-field communication (“NFC”), Wi-Fi, or by programming,the knob 2000 may be placed at any convenient location, such as on theperson of a user (such as where a user is moving from place to place inan industrial environment), on a dashboard or other control system thatcontrols multiple devices, or on another object. The knob 2000 may beprovided with alternative interfaces for being disposed, such as clipsfor attachment to objects, hook-and-loop fasteners, magnetic fasteners,and physical connectors.

The smart knob 2000 may use, include or control the various features ofthe smart cooking systems 21 described throughout this disclosure.Additionally, the smart knob 2000 may be connected to other IoT devices,such as smart doorbell, remote temperature probe (e.g., in arefrigerator or freezer), and the like. The smart knob 2000 may be usedfor kitchen tasks other than cooking. By connecting with a temperatureprobe, the smart knob 2000 may be used to inform a user of the progressof an item placed in the refrigerator or freezer to cool down.

As it requires only very little power and as it is mountable on thesmart cooking system 21, the smart knob 2000 may, in embodiments, berecharged through thermoelectric conversion of the heat from a burner onthe cooking system 21, so that the use of external power supply is notrequired.

FIGS. 188-195 depict a variety of user interface features 2010, 2020,2101, 2201, 2300, 2400, 2500, 2600 of the smart knob 2000.

FIG. 196 depicts a smart knob 2700 deployed on a single heating elementcooking system 2710, while FIG. 197 depicts a smart knob 2800 placed ona side of a kitchen appliance 2810.

Other features of a smart cooking system 21 may include examples ofsmart temperature probes 3101 depicted in FIGS. 198-201. The temperatureprobe 3101 may consist of a wired or wireless temperature sensor thatmay be interfaced with a smart cooking system 21, smart knob 2000,and/or a mobile phone 150 for cooking. The temperature probe 3101 may,in embodiments, be dipped into a liquid (such as a soup, etc.) orinserted interior of a solid (such as a piece of meat or a cooking bakedgood), to cook very precisely based on the measured interior temperatureof the liquid or solid. Also the smart temperature probe 3101 mayfacilitate use of an induction base to control the temperature of thebase for heating water to a precise temperature (e.g., for tea) with anytype of non-magnetic cooking vessel.

The smart cooking system 21 may include a smart phone docking station3301 that may be configured to prevent cooking heat from directlyimpacting a device in the station while facilitating easy access to thephone for docking, undocking and viewing. A variety of different docks3310, 3401, 3501, 3601, 3701, 3801 for compatibility with a range ofsmart phone and tablet devices are depicted in FIGS. 202-207.

Various burner designs are contemplated for use with a smart cookingsystem as described herein. FIGS. 208-224 depict exemplary burners 3900,4200, 4701, 5000, 5300.

The Internet-connected smart cooking system 21 described herein mayinclude tools and features that may help a user, such as a homemaker, acommercial chef, or cook in an industrial environment to preparehealthier meals, learn about food choices of other users, facilitatereduced meal preparation time, and repeatable cooking for improvedquality and value. A few applications that may leverage the capabilitiesof the present Internet-connected smart cooktop may include a fitnessapplication that helps one estimate daily calorie consumptionrequirements for each member of a user's family or other person for whomthe user may prepare meals. This may help a user to control and trackthe user's family fitness over time. Using data from recipes and weightsensors for pots/pans used to cook the food for the recipes, a fitnessapplication may generate a calorie consumption estimate and suggest oneor more healthy alternative recipes. Through combining sensing andcontrol of the cooktop functionality (e.g., burners) with Internetaccess to food nutrition and weight values for recipe ingredients beingcooked, the calorie count of a content of a pan placed on a smartcooktop burner may be estimated. As an example, if a recipe calls for ¼cup of lentils per serving combined with a serving-unit of water, atotal weight of a pan being used to prepare the lentils may be sensed.By knowing the weight of the pan, a net weight of the ingredients in thepan may be calculated so that a number of servings in the pan may bedetermined by calculating the total weight and dividing it by a weightper serving. By accessing recipe comparison tools (e.g., as may beavailable via resources on the Internet) that may include lists ofcorresponding meals that have lower fat, higher nutritional ingredients,alternate recipes could be suggested to the user that would providecomparable nutrition with lower calories or fat, for example.

A food investigation application may gather information from the smartcooktops and user activity about recipes being used by users of thesmart cooktop systems throughout a region (e.g., a country such asIndia) to calculate various metrics, such as most often cooked recipe,preferred breakfast meal, popular holiday recipes, and the like. Thisinformation may be useful in planning purposes by food suppliers,farmers, homeowners, and the like. As an example, on any given day,information about the recipes that people in your region are preparingmight be useful in determining which dishes are trending. AnInternet-based server that receives recipe and corresponding limiteddemographic information over time may determine which meals aretrending. A count of all uses of all recipes (or comparable recipes)during a period of time (e.g., during evening meal preparation time) maybe calculated and the recipes with the greatest use counts could beidentified as most popular, currently trending, and the like.

Cooking becomes more repeatable so a cook (e.g., a less experiencedcook) may rely on the automation capabilities of an Internet-connectedsmart cooktop system to avoid mistakes, like overcooking, burning due toexcessive heat, and the like. This may be possible due to use ofinformation about the items being cooked and the cooking environment,such as the caloric output value of each burner in any heat outputsetting, the weight of the food being cooked, target temperature andcooking time (e.g., from a recipe), a selected doneness of the food, andthe like. By combining this information with modeled and/or sensedburner operation (e.g., temperature probes may be used to detect thetemperature of the food being cooked, the temperature of the cookingenvironment, and the like) to facilitate automated control of heat,temperature, and cooking time thereby making meal cooking repeatable andpredictable. Each type of burner (e.g., induction, electric, LP gas,hydrogen gas, and the like) may each be fully modeled for operationalfactors so that cooking a recipe with induction heating today and withhydrogen gas heat tomorrow will produce repeatable results Similarcapabilities to combine information from the cooking system andinformation from sensors or other systems may be used to improverepeatability and improvement of industrial processes, such asmanufacturing processes that produce materials and components throughheating, drying, curing, and the like.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with hydrogen production, storage, and usesystems. In embodiments, the hydrogen production, storage, and usesystems may use renewable energy as a source of energy for variousoperations including hydrogen production, hydrogen storage,distribution, monitoring, consumption and the like. In embodiments,hydrogen production, such as with a hydrolyzer system, may be powered byrenewable energy such as solar power (including systems using directsolar power and photovoltaic systems (including ones usingsemiconductors, polymers, and other forms of photovoltaic), hydro power(including wave motion, running water, or stored potential energy),gravity (such as involving stored potential energy), geothermal energy,energy derived from a thermal gradient (such as a temperature gradientin a body of water, such as ocean water, or a temperature gradientbetween a level of the earth, such as the surface, and another level,such as a subterranean area), wind power and the like and whereapplicable. References to renewable energy throughout this disclosureshould be understood to encompass any of the above except where thecontext indicates otherwise.

In embodiments, solar collector panels or the like may be configuredwith a hydrogen production system, such as a system described herein, toprovide electricity for powering the production of hydrogen, includingfrom water. A hydrogen production system may be built with integratedsolar collector panels and the ability to connect to further solarsystems, so that placement of the hydrogen production system in anambient environment that is exposed to sunlight may facilitate itsself-powered operation or partially-self-powered operation via solarpower.

In embodiments, solar power harvesting subsystems, such as a singlepanel or an array of solar panels, may be configured to be deployedseparately, and optionally remotely, from the hydrogen productionsystem. Solar power harvesting subsystems may be connected to one ormore hydrogen production systems to facilitate deployment inenvironments with localized limited access to sunlight, such as in amulti-unit dwelling, a building with few windows, a building withinterior areas that do not receive direct or sufficient sunlight (suchas a warehouse, manufacturing facility, storage facility, laboratory, orthe like) and the like. Other operational processes of a system forhydrogen production, storage, and use may be powered via solar power.

Solar energy harvested for the production of hydrogen may be sharedand/or diverted to these other operations or sold back into the localgrid as needed. Solar energy harvesting may also be used to charge abattery, charge various thermal systems, or other electrical energystorage facility that may directly provide the energy needed forhydrogen production immediately or with a time-shift and on-demandfunctions and other operational elements as described herein. In thisway, while solar power provides a renewable source of energy, the impactof an absence of sunlight and therefore diminished solar powerproduction may be mitigated through the use of an intermediate batteryor the like.

In embodiments, a data collection system, involving one or more sensorsand instruments, may be used to monitor the solar power system orcomponents thereof, including to enable predictive maintenance, toenable optimal operation (including based on current and anticipatedstate information), and the like. Monitoring, remote control, andautonomous control may be enabled using machine learning and artificialintelligence, optionally under human training or supervision, as withother embodiments described herein. These capabilities for datacollection, monitoring, and control, including using machine learning,may be used in connection with the other renewable energy systems, andcomponents thereof, described throughout this disclosure.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with other sources of renewable energyincluding wind power. Wind power may be harvested through a windmill,turbine, roots-blade configuration, or similar wind power collectionfacility that may be configured with the hydrogen production, storageand use systems and components similar to a solar collection facility orother electric sources as described herein. In many examples,configuring a turbine or similar wind power collection and conversiondevice attached to a hydrogen production, storage, and use system mayfacilitate deployment in a variety of environments where sufficientmoving gas (such as blowing wind, air flowing around a moving element(such as part of a vehicle), exhaust from an industrial machine orprocess, or the like) is available. These and other embodiments areintended to be encompassed by the term “air flow” in this disclosureexcept where the context indicates otherwise.

In embodiments, a variety of sources of air movement may be utilized asa source of power from the air flow. In various examples, heated airthat may result from the use of the hydrogen, such as for cooking andthe like, may pass through a wind harvesting facility, such as a turbinethat may be disposed in the heated air flow path. In embodiments, otherheat harvesting devices may be deployed such as positive displacementdevice or other heated mediums through which energy may be absorbed andpower a suitable heat engine. In embodiments, disposing a turbine orother energy/heat harvesting devices directly above a stove, cookingsystem, or other heat generating use of the hydrogen produced mayproduce energy that may be used to power, directly or indirectly,partially or wholly, such as through recharging a battery, operationalprocesses of a hydrogen production, storage and use system.

In yet another use of renewable energy for powering one or moreoperational processes of a hydrogen production, storage, and/or usesystem, such as may be described herein, hydropower may be a source ofrenewable energy. In embodiments, hydropower may be converted into aform that is usable to operate processes of a hydrogen production,storage and use system as described herein including electricalproduction and possibly harvesting mechanical power. In these examples,electricity from hydropower may be utilized to operate a hydrolyzer toproduce hydrogen from a hydrogen source, such as water or ambientair-based water vapor. In embodiments, configuring a hydrogenproduction, storage, and use system that may directly utilize hydropower may involve building an enclosure that keeps a source ofhydropower, such as a moving body of water (e.g., a river, waterfall,water flowing through a dam, and the like) from interfering with theoperational processes such as hydrogen production, storage, and use. Inembodiments, such an enclosure may facilitate deployment of ahydropower-sourced system directly in a flow of water by making at leastportions of such a system submersible. Hydrogen production and storage,for example, may benefit from such an enclosure. In particular, asubmersible hydrogen production system may take advantage of thehydrodynamic water in which the system is submerged as a source ofhydrogen, as a source of energy to produce the hydrogen, as a source tocool the process, or the like.

Referring to FIG. 226, embodiments of the methods and systems related torenewable energy sources for hydrogen production, storage, distributionand use are depicted. A system the facilitates use of renewable energyas described herein may include a hydrogen production facility 5074 thatmay be coupled to a hydrogen storage facility 5703. The hydrogenproduction facility 5705 and/or the hydrogen storage facility 5703 maybe coupled to one or more hydrogen use facilities 5707. One or more ofthe hydrogen use facilities 5707 may be coupled through a hydrogendistribution network (not shown).

Hydrogen production, storage, distribution, and use may be at leastpartially powered by one or more renewable energy sources, such as solarenergy source 5709, wind energy source 5711, hydro energy source 5713,geothermal energy source 5715, and the like. A wind energy source 5711may be natural air currents, motor driven air currents, air currentsresulting from movement of a vehicle, or waste air flow sources 5719(such as waste heat from heating operations, such as cooking and thelike). Any of these renewable energy sources may be converted into aform of energy that is suitable for an intended use by the hydrogenproduction, storage, distribution, and use system. As an example, asolar energy source 5709 may be converted to electricity as describedherein to provide electrical power to the hydrogen production facility5705, hydrogen storage facility 5703, use facility 5707 and the like. Itwill be appreciated in light of the disclosure that the hydrogen storagefacility 5703 need not be required to operate with the hydrogenproduction facility 5705 and the hydrogen use facility 5707 as theproduced hydrogen may be consumed upon its production without a need forstorage.

Another form of energy that may be sourced by the hydrogen productionfacility 5705 may include a sulfur dioxide source 5717, such as fossilfuel combustion systems that produce waste sulfur dioxide. As describedherein, a sulfur dioxide source 5717 may supply heat energy and rawmaterial from which hydrogen gas may be produced by a hydrogenproduction facility 5705 adapted to use sulfur dioxide.

Yet another form of energy that may be sourced by the hydrogenproduction facility 5705 and/or storage facility 5703 may include heatrecapture 5721 from one or more of the hydrogen use facilities 5705. Therecovered heat may be used directly, converted into another form, suchas steam and/or electricity, or provided as input raw material fromwhich hydrogen may be harvested.

Referring to FIG. 227, an alternate embodiment of renewable energy usewith at least one hydrogen production facility 5705, at least onehydrogen storage facility 5703. In the embodiment of FIG. 227, hydrogenproduction, storage, distribution, and uses may be connected, but maynot be integrated, such as into a standalone combined function system.In the embodiment of FIG. 227, renewable energy sources as described forthe embodiment of FIG. 226 may be used to provide energy for hydrogenproduction 5705 and storage 5703. However, hydrogen use may be providedthrough a hydrogen distribution system 5823 that may be coupled to thehydrogen production facility 5705, storage facility 5703 and to hydrogenuse facilities 5707 that may be located at distinct physical locations,such as individual apartments in an apartment building, and the like.

Referring to FIG. 2228, the methods and systems described herein forhydrogen production, storage, distribution, use, and control may becoupled with predictive maintenance methods and systems to facilitateimprovements in operation with less unplanned downtime and fewercomponent failures. In the embodiment of FIG. 229, predictivemaintenance facility 5903 may be configured to operate on a processorassociated with or more particularly integrated with a hydrogenproduction, storage, and use facility. Alternatively, predictivemaintenance facility may be configured to operate on a processor that isnot integrated, such as a cloud computer, a stand alone computer, anetworked server, and the like. Predictive maintenance facility 5903 mayreceive input from various system sensors 5905 along with informationfrom various data sets, such as a use/maintenance model 5915, warrantyand standards rules 5919, and an archive of sensor data and analyticsderived there from 5917, among other sources.

System sensors 5905 may include hydrogen system sensors, input energysensors, process sensors (e.g., catalytic sensors and the like), outputsensors, use sensors, and a range of other sensors as described herein.Each or any of these sensors may provide data directly or through anintermediate processor a data acquisition unit, a cross-linked dataacquisition unit, and the like to the predictive maintenance facility5903. For a local/integrated predictive maintenance facility 5903,sensor data may be provided through a range of inputs, including directinputs and the like. For a remote/cloud preventive maintenance facility,sensor data may be provided through a networking interface, such as theInternet, an intranet, a wireless communication channel, and the like.

The predictive maintenance facility 5903 may further be coupled with alocal or remote user interface for providing reports, facilitatingcontrol, interacting with the predictive maintenance facility 5903 tofacilitate user participation in maintenance actions, planning, andanalysis. The user interface facility 5909 may be integrated with thepredictive maintenance facility 5903, such as being an integratedcomponent of a hydrogen production, storage, and use system.Alternatively, the user interface 5909 may be remotely accessible, suchas through a network, a cloud network facility, and the like includingwithout limitation the Internet and the like.

To facilitate at least semi-automated predictive maintenance,replacement parts, service, and the like may be automatically orderedbased on a result of the predictive maintenance facility 5903 indicatingthat some form of preventive activity is required. The automaticpart/service ordering facility 5913 may be connected directly orindirectly to the user interface/control facility 5909 to enable usersto approve or adjust an automated order.

The embodiments of FIG. 229 include at least two configurations; (i) anintegrated hydrogen cooking/heating system with predictive maintenance5911, and (ii) modular system that may take advantage of sharedresources such as cloud computing capabilities, cloud storage facilitiesand the like.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with one or more computing devicefunctions that interface with operational, monitoring, and otherelectronic aspects of a hydrogen production, storage and optional usesystem as described herein and that may be accessed through a variety ofinterfaces. Functions, several of which are described elsewhere herein,may include control and monitoring of hydrogen production, control, andmonitoring of hydrogen storage including distribution and the like,control and monitoring of the use of generated and/or stored hydrogen.In embodiments, access to these functions, such as to provide controlinput and receive monitor output, may be done through an interface, suchas an application programming interface (API) or an interface to one ormore services, such as in a services oriented architecture, that mayexpose certain aspects of these functions, services, components, or thelike, to facilitate access thereto. The terms “API” or “applicationprogramming interface” should be understood to encompass a variety ofsuch interfaces to programs, services, components, computing elements,and the like except where the context indicates otherwise.

In embodiments, API type interfaces may include a library of features,such as algorithms, software routines, and the like through which theexposed aspects may be accessed. In embodiments, API type interfaces mayfacilitate access to a control function of a hydrogen productionsubsystem as described herein to enable third-party control and/ormonitoring of the subsystem, to facilitate analytics with outsideresources, to facilitate interconnection of multiple resources,coordination of fuel and renewables between multiple systems, and thelike. In embodiments, a single hydrogen production subsystem may beutilized to provide hydrogen to a plurality of hydrogen storage systems.By way of these examples, one or more of the hydrogen storage systemsmay use the API or API-type interface to access a flow valve, fueldistribution architecture, or the like that may facilitate distributionof hydrogen produced by the storage systems so that storage systems thatare at or near storage capacity may direct a control function of theflow valve to reduce or stop distribution of the hydrogen to the storagesystem. In embodiments, Application programming interfaces may beutilized across a range of control and monitoring functions, includingproviding access to hydrogen consumption monitoring elements, renewableenergy utilization monitoring systems, hydrogen use systems, smartcooktop systems as described herein, and the like.

In addition to API type interfaces as described herein, a hydrogenproduction, storage, and use system may be accessed through one or moremachine-to-machine interfaces. In embodiments, such interfaces mayinclude directly wired interfaces, such as between a monitoring machineand a sensor disposed to sense the flow of water, the flow of energyused for hydrolysis, the flow of resulting hydrogen, or one or morelevels, such as liquid levels, of any of the foregoing. In embodiments,machine-to-machine interfaces may be indirect, such as through astandard communication portal such as network, e.g., an intranet, anextranet, the Internet, and the like. In embodiments, communicationprotocols such as HTTP and the like may be utilized to exchange control,monitoring, and other information between some portion of the hydrogenproduction, storage, and use system and another machine. In embodiments,a machine-to-machine interface may facilitate third party control ofhydrogen use. This may manifest itself in a variety of modes, examplesof which may be a user remotely accessing a cooking function from hismobile device using the Internet as a machine-to-machine interfacebetween the mobile device and the cooking function.

In embodiments, interfacing with a hydrogen production, storage and usesystem as described herein may also be accomplished through a graphicaluser interface (GUI). In the many examples, such an interface mayfacilitate human direct access to control, monitoring, and otherfeatures of the system. In embodiments, a GUI may include a variety ofscreens that may be logically related to facilitating user access to arange of features of the system within a single GUI. In the manyexamples, there may be a main system GUI screen that may include linksto a main production GUI screen that may include, among other things,links to further production GUI screens, e.g., a main screen may link toan energy source control screen, a storage system control, systemhealth, predictive information, and the like. In embodiments, a main GUIscreen may also facilitate accessing one or more GUI screens for otheraspects of the system, such as hydrogen storage monitoring and control,hydrogen distribution monitoring and control, hydrogen use, cookingfunctions of a smart cooktop, heating functions for a heater subsystem,and the like.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with predictive maintenance functions thatmay facilitate smart replacement of components thereby avoiding failureand down time. In embodiments, predictive maintenance functions that aredescribed herein may be further enhanced using one or more sensors thatmay facilitate monitoring and/or control of portions of the system thatmay require maintenance. In the examples, one or more sensors may bedeployed that facilitate monitoring and/or control of an electrolyzerfunction. By way of the examples, the one or more sensors that maymonitor the membrane portion of the electrolyzer may provide data thatmay be useful for detecting one or more conditions that requiresattention immediately or may culminate with other factors and may laterrequire attention, such as a condition that requires the membrane to bereplaced. Such sensors may further be configured to generate one or morealerts, such as audio, visual, electronic, logical signals when sensinga condition that may indicate replacement of the membrane or otherportion of the hydrolyzer is recommended. Such sensors may further beconfigured to generate one or more alerts that may trigger one or morerecordings of data from the sensors for a long duration to capturesignals that may capture events at various intervals, frequencies, andmagnitudes that may be indicative of the need to replace the membrane orother portion of the hydrolyzer. Examples of the membrane and theelectrolyzer are disclosed in U.S. Pat. No. 8,057,646 to Hioatsu, et al,filed on 7 Dec. 2005, and U.S. Pat. No. 6,554,978 to Vandenborre, filed1 Jun. 2001, each of which is hereby incorporated by reference as iffully set forth herein.

In embodiments, such alerts may be generated by the sensors and/or byone or more computing facilities that may interface with the sensors andmay analyze data from the sensors. In embodiments, sensors, such as amembrane sensor, may be integrated into the system physically (tomonitor a physical aspect of the system), and/or logically (such as analgorithm that processes data from one or more sensors). In embodiments,one or more membrane sensors, or the like, may detect one or moreconditions that may be indicative that another action or precautionshould be taken. In embodiments, one or more alerts from such sensorsmay indicate the type of condition sensed as well as a degree of thecondition sensed. In embodiments, when sensor alert and/or sensor datais combined with other information known about the system, an alert maybe generated that indicates one or more actions or precautions thatshould be taken to counteract the condition causing the alert. In oneexample, an alert (or set of alerts) may require an action to reduce anamount of hydrogen being produced, such as by turning off or cyclingwith a greater duty cycle the operation of the hydrolyzer.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with sensors that may monitorinterconnections for corrosion or other conditions, such as internalbuildup that reduces the flow of hydrogen or the like through theinterconnections that may be associated with the system. In embodiments,such sensors may provide data indicative of a degree of corrosion,conditions that might speed corrosion, and the like to a computingdevice that may detect a condition indicative of needing to take actionimmediately or at such time as the degree of corrosion would demand suchas replace an affected portion of the interconnections. In an example,the one or more conditions may be determined by comparing data from theone or more sensors with data values that suggest an unacceptable degreeof corrosion.

In embodiments, a monitoring subsystem with one or more sensors maycollect, analyze, and/or report the real-time measurement of senseddata. Likewise, such a subsystem may collect, analyze, and/or reportreal-time failure data, such as to facilitate measuring and/or trackingmaterial failure data, e.g., frequency, degree, time since deployment,and the like.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with other sensing modalities to monitorcatalytic activities to determine, for example, catalytic performance,efficiencies and the like. Based on these sensed activities, alerts thatmay indicate a need for catalyst replacement and/or other actions orprecautions to be performed may be generated.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with various methods and systems tomonitor and determine input demand, output production, need forincreases therein, and the like.

In embodiments, a facility with multiple hydrogen operations includingproduction and/or storage may be shown to benefit from monitoring tobalance storage and production rate capacity, such as for variabledemand. In embodiments, monitoring input demand may provide insight intothe amount of hydrogen being used, when it is used, with what othergases it is being used, which use subsystems are demanding input,quality of hydrogen produced, amount of energy required to produce thehydrogen, rate of hydrogen production and use over time and under avariety of conditions, and the like. In embodiments, sensors may bedeployed and integrated with monitoring and control systems to monitorand coordinate efficient and safe storage or transfer of hydrogen.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with one or more sensors to monitor andcoordinate efficient and safe storage and/or transfer of hydrogen may beimplemented in the Internet of things (IoT) applications. In exampleswhen hydrogen is stored as part of a micro/smart grid solution,monitoring system functions, such as input demand, production, andstorage may facilitate determining a need for increasing input/supply.Likewise, sources of energy for operating a hydrolyzer and the like asdescribed herein, such as renewable energy from solar and wind may bemanaged so that available sunlight and/or the wind may be tied tohydrogen production demand predictions from users such as industrial andothers. In embodiments, this may facilitate ensuring allocation ofavailable hydrogen for grid stability and the like. In embodiments,sensors that measure integrated energy use may similarly provideinformation to further facilitate managing for grid stability, amongother things. In examples, predicted demand may be used in determiningwhen and how much hydrogen should be produced and whether it should bestored to facilitate grid stability. In embodiments, this informationmay be used when portions of a grid are predicted to have high demand,while other portions are predicted to have low demand. Supply, from theproduction of hydrogen and/or from stored hydrogen, may be directedwhere when it is predicted to be needed or it is predicted to be neededin possibly relatively fewer quantities but may be consumed morequickly.

In embodiments, another form of system sensing may involve fuel qualitysensing. In embodiments, sensors that may accurately measure fuel andoxidant compositional characteristics may be used in a control system todirect hydrogen to different storage facilities based on theinformation. By way of these examples, uses of hydrogen that maytolerate higher oxidant composition may be sourced from storagefacilities appropriately, perhaps at a lower cost than for hydrogen witha lower oxidant composition.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with sufficiently reliable flamemonitoring systems that may sense one or more of flame quality, flamestability, flame temperature, and the like. In embodiments, the methodsand systems disclosed herein may include, connect with or be integratedwith one or more sensors that may provide for continuous flue gasanalysis that may be used to adjust the efficiency and magnitude theflame. In embodiments, further sensors and control systems related toflame or combustion products monitoring may be used including one ormore continuous heat flux meters.

In embodiments, the methods and systems disclosed herein may include,connect with or be integrated with one or more particle sensors todetermine how clean something is, e.g., exhaust and/or ambient releasefrom a process or liquid including from hydrocarbon combustion. Inembodiments, one or more emission detection sensors may be used detectinefficient combustion and may also be used to detect leaks from thesystem. By way of these examples, the one or more sensors may beconfigured to measure partial pressure or particle count when sensinginternal and/or external emission such as diatomic hydrogen, carbondioxide, carbon monoxide, and other combustion byproducts. The one ormore sensors may be configured to measure combustion wave front,cylinder head temperature, lubrication cleanliness and/or entrainment,various vibration signals that may be indicative improper operation.

In embodiments, methods and systems that may include, connect with, orbe integrated with hydrogen production, storage, and use may be deployedin a variety of environments. Systems that may facilitate production ofa consumable energy source, such as hydrogen gas may be utilized inenvironments such as cooking meals or food preparation heating and/orcooking processes, including without limitation industrial cooking.

Preparation of meals or of food items that may be stored long term, suchas canned foods and the like may be performed with the methods andsystems described herein. Preparation of meals or food items inenvironments in which direct access to a reliable source of energy, suchas electricity, natural gas, or other household combustibles for cookingor otherwise is not readily available, such as in mobile, sea-borne,air-borne, and other environments that are often actively in travel maybe shown to benefit from the methods and systems described herein forautonomous production of hydrogen gas for use as a cooking energysource. Use of a cooking system that is described herein may bebeneficial for use in mobile environments by reducing a total amount offuel to be stored for use while in motion. By producing a clean burningenergy source, such as hydrogen from renewable energy sources andthrough harvesting hydrogen from an ambient environment, deploying suchsystems on long duration travel vehicles, such as cargo ships, militaryships, submarines, and the like may reduce the payload required to becarried for purposes such as meal preparation, cooking and the like.

Renewable energy to power processes of hydrogen production, monitoring,storage, distribution, and use may be harvested through the methods andsystems described herein including solar power harvesting, wind powerharvesting, thermal (e.g., geothermal) when deployed in mobileenvironments. Solar energy harvesting systems or components thereof thatmay be included with, connected to, or integrated with the hydrogenproduction, storage and use systems described herein may be deployed onsun-exposed surfaces, such as a roof of a vehicle, aircraft, ship, andthe like. Air movement around and/or through a moving vehicle, as aresult of propulsion of the vehicle and the like may be harvested andconverted into an energy source suitable for use with hydrogenproduction, storage, distribution and the like. Heat generated by mobilesystem propulsion systems may be converted into a form of energysuitable for use in production, storage, distribution, and use ofhydrogen. This may be accomplished through the use of inline turbinesystems, other heat and energy extraction machines, wind capturesystems, exhaust heat recapture systems, and the like. By using thesereadily available sources of energy, many of which are not otherwiseutilized, total external energy requirements that may only be metthrough onboard storage, may be significantly reduced.

Use of the methods and systems for hydrogen storage and use may includedeployment in marine transportation, such as on a submarine where thegeneration of toxic waste gas is undesirable. Hydrogen gas may beproduced from sea water, stored as needed onboard, and safely consumedfor cooking and other heating uses in a submarine without risk or costsof dealing with waste gas cleansing or removal. The hydrogen gas may beproduced from sea water but not stored any only generated and consumedas needed onboard, and safely consumed for cooking and other heatinguses in a submarine.

Other environments of deployment of the hydrogen-based systems describedherein may include use on aircraft, such as for preparation of meals tobe consumed on the flight. Other aircraft-based uses may includeindustrial cooking while in-flight to, for example, produce cooked goodsfor use, storage or distribution after the aircraft returns to earth.Inflight-based cooking with the methods and systems for autonomoushydrogen cooking systems and the like described herein may facilitatecooking food and the like for extended duration flights, such asaircraft that remains aloft rather than just being operated from onelocation to another. Meals, foods, and other goods could be cooked whilein-flight may be transported to/from the in-flight aircraft throughshuttle or other aircraft to facilitate longer duration flights.

Earth-bound operations such as drilling and mining that may have verylimited access to cooking fuel or other commercially available fuelsources may be shown to benefit from the use of such a system. Equipmentthat transports materials, supplies, and workers to/from subterraneandrill sites and mines may be equipped with such a system to facilitatepreparation of food for the workers. Use of a fuel, such as hydrogenthat produces no toxic exhaust may be well suited for use in drillingand mining environments.

Agricultural production, including harvesting, planting, and the likemay also benefit from the deployment of hydrogen-based cooking and/orheating systems as described herein. Food preparation operations thatmay include heating or cooking freshly harvested foods may be shown tobenefit from an automated or semi-automated hydrogen-based cookingsystem as described herein. Such a system may be deployed on orconnected with a harvesting system, such as a produce harvester and thelike so that cooking, preserving, sterilizing, pasteurizing, drying oroptional storage operations may occur as the food is harvested. Otherdeployments, such as industrial cooking deployments, may includejob-site deployment, food truck deployment, canteen truck deployment,food production pipelines, and the like. Yet other deployments, such asindustrial cooking deployment may include residential environments, suchas nursing homes, group homes, soup kitchens, school and businesscafeterias, disaster relief food preparation stations, and the like.

The methods and systems of autonomous or semi-autonomous hydrogenproduction, storage, distribution, and use may be deployed as componentsin a smart power grid that may operate cooperatively with othercomponents of a smart grid to attempt to deliver reliable energyavailable throughout the grid. In an example, a renewable energy-basedhydrogen production system may utilize its renewable energy harvestingcomponents to deliver electricity to a smart grid based on variousfactors, such as local demand for hydrogen and the like. When arenewable energy source is available, yet hydrogen production is notcalled for (e.g., sufficient supply is stored, or an amount that isanticipated to be needed, such as based on machine learning or the likeof prior local hydrogen demand over time is expected to be produciblebefore needed), then electricity or the like produced from the renewableenergy source could be fed back into the smart grid.

Other types of industrial applications of the methods and systems ofhydrogen production, storage, distribution and use may include air andinline heaters, and the like. Exemplary environments may includedeployment for aerospace operation and testing, such as componenttemperature testing, heating, hot air curing, and the like. Productionof temperatures that emulate extremes associated with aerospace travel,such as earth atmosphere entry and the like could be replicated withsuch systems for use in component testing and the like.

Other industrial heating applications may include automotive production(e.g., heat treating components, heat shrinking and the like),automotive assembly (e.g., hot air bonding, etc.), automotive exteriorand interior customization (e.g., hot air bonding of vinyl body panelcovers, paint curing and the like), and automotive repair (e.g.,reshaping dented plastic components, such as a bumper) and the like.

Yet other industrial heating applications may include packaging,sterilization, and the like. Particular packaging uses may includehigh-speed poly-coated paperboard sealing, high-speed heat shrinkinstallations, material heat forming, curing adhesives, sterilizingbottles and cartons (e.g., through heating water and/or steamtherefore), production and packaging of pharmaceuticals, sterilizationand packaging of surgical tools and hardware, replacement dentalfeatures (e.g., crowns and the like), production and sealing ofpackaging material, and the like.

Paper and printing heating-related applications of the methods andsystems described herein may include the production of coated paper,including speed drawing the coating, adhesive activation, ink drying,paper aging, pulp drying, and the like.

Plastics and rubber production heating applications that may be shown tobenefit from the methods and systems described herein may include rubberextrusion salt removal, curing plastics, bending and forming plasticcomponents, de-flashing of molded parts and the like.

The methods and systems described herein may be used to produce heatneeded for some semiconductor and electronics production and assemblyoperations including soldering operations, such as air knife for wavesoldering, heating of printed circuit boards, lead frames, components(e.g., capacitors) for soldering/desoldering, centralized source of heatfor a multi-station desoldering system, wafer and PC board drying, heatshrink wire insulation, preheating process gases and the like. By way ofthese examples, soldering and/or brazing may require heating that may beprovided by the hydrogen-based heating systems described herein. Heatfor soldering and brazing may be generated locally at each brazingstation or may be provided from a centralized source for multiplesoldering operations, including manual and semi-manual operations.

Other heated air applications that may be suitable for application of ahydrogen-based system as described herein may include textilesindustrial uses, such as welding plastic or vinyl fabrics, heat-treatingspecialty fabrics, heat sealing fabric shipping sleeves, bondingmulti-ply fabrics and the like. Industrial hot air applications mayinclude the exemplary embodiments described herein, but may also includeother comparable applications, such as home fabric bonding, plasticsheet dispensing and the like in which heat is used to increase thetemperature of air or devices to perform various functions.

In embodiments, the methods and systems described herein that relate tohydrogen production, storage, distribution, use, regulation, monitoring,control, energy conversion, and the like may also be used for heatingoperations including immersion, circulation and customer heating.Example applications include energy production environments where fuelsources for cooking and heating may be used, such as alternative fuelsprocessing, chemical processing, mining and metals, oil, and gas,petrochemical, power generation, fuel storage, fuel distribution, heatexchangers, waste disposal, heated storage, and the like. Industrialapplications may include biopharmaceutical processing, industrialequipment (such as temperature test chambers), engine block heaters,preheating industrial burners, furnaces, kilns and the like, medicalequipment laboratory and analytic equipment, military and defenseincluding weapons, personnel management, and other military uses,production of rubber and plastics through controlled heating ofpetrochemicals and the like, transportation (such as passengercompartment temperature regulation, preheat or temperature regulation ofvehicle systems in extremely low temperature environments) and the like,water processing, waste water processing and the like. Commercialapplications of the methods and systems described herein for use asheating for immersion, circulation and the like may include integration,connection or use with commercial food equipment, building andconstruction systems, commercial marine and shipping systems andenvironments, heat-powered cooling, refrigeration, air conditioning, andother cooling applications and the like.

In addition to cooking and air heating applications, the methods andsystems of autonomous hydrolyzer operation, generated fuel storage,distribution and use described herein may also be applied to processesthat use heat from a heating element that may be powered from the fuel(e.g., hydrogen and the like) produced from the hydrolyzer.Manufacturing operations may include pharmaceutical manufacturing,industrial food manufacturing, semiconductor manufacturing, and thelike. Other heating element-like applications may include coating suchas vinyl automotive panel wrapping, molding such as injection molding,heat staking, and the like, hard tooling, heating material for extrusionoperations, combustion systems (such as flame-based combustion devices,e.g., burners that would improve on existing combustion methodsincluding improving efficiency, cost, reduce or eliminate emissions),enhance heat transfer from combustion products to the material processedfor a variety of applications, such as by applying a clean-burning fuelin proximity to the material being processed, other types of combustionsystems (e.g., non-burner types) such as catalytic combustion,combustion systems that include heat recovery devices such asself-recuperative burners, and the like.

Other applications for heat-dependent operations that may be powered bythe fuel produced from a hydrolyzer may include heat and power uses suchas integrated heating systems such as super boilers and otherapplications that deliver both heat and power to an operation (e.g.,super pressurized steam systems, and the like). Other heat utilizationapplications may include heat production include use for testingmaterials such as products for mining (e.g., heat treating drillingmachine elements), drying and moisture removal (such as clothes dryers,dehumidifiers, and the like). Other applications in which ahydrolyzer-based energy producing system may be used include heat as acatalyst for chemical reactions and processing including, withoutlimitation chemical scrubbing of exhaust from industrial systemsincluding petrochemical-based combustion systems, on-site production ofchemicals, such as high-value petroleum products from lower grade, lowercost petroleum supplies, and the like.

Other applications that may benefit from the use of an autonomoushydrogen generation system as described herein may include desalination,such as local desalination systems for pleasure boats, ferries, and thelike. Because of the high efficiency and potential for only usingrenewable energy sources, hydrogen generation-based desalination systemsmay be fully self-operative, producing hydrogen directly from a sourceof water being desalinated.

Yet other applications include using heat to power carbon capture,purification of material and systems such as a palladium electrolyzer,and the like. Industrial washing systems, such as laundry, preheatingboiler water feeds, sterilizing, sanitation, and cleaning processes forclothing, uniforms, safety gear, hospital and medical care facilities(e.g., floors and the like) may also be target applications for systemsthat include, connect to, or integrate hydrogen production, storage, anddistribution, including systems that are powered by renewable energysources and the like.

Filtering and purifying materials and equipment used in variousprocesses, such as food service, food manufacturing, pharmaceuticalproduction and handling, livestock handling and processing and the likeare also candidate application environments for the methods and systemsdescribed herein. In production environments that may rely on highlypurified materials, such a system may be applied to provide thenecessary heating or energy required. In embodiments, the methods andsystems described herein may be applied to corrosion and hydrogenembrittlement activities.

Referring to FIG. 229 environments and manufacturing uses of hydrogenproduction, storage, distribution, and use systems are depicted. Asdescribed above herein, hydrogen system 5701 may be deployed inenvironments including industrial cooking 6006, industrial air heatersand inline heaters 6009, and industrial environments 6011. A hydrogensystem 5701 may also be used in manufacturing use cases 6005, such asheat used in manufacturing processes 6013. Deployment in environments6003 and manufacturing uses 6005 may overlap, resulting in a hydrogensystem 5701 operating in combinations of environment and use that aredepicted in FIG. 229 and described herein.

The methods and systems described herein may be used to provide hydrogendirectly from a hydrolyzer for certain uses including uses that do notrequire the introduction of oxygen. In such embodiments that may onlyrequire a hydrogen gas, the hydrogen may be produced and sent directlyfor real-time uses such as a burner for heating, industrial heatingprocesses like welding and brazing, and all other use cases that requiredirect-use hydrogen. Some other cases may include coating, tooling,extrusion, drying and the like. The methods and systems described hereinmay produce high-quality hydrogen gas for applications that require it,such as laser cutting. Other uses may include the production of hydrogengas that may then be combined with other combustible gases foroperations such as to generate a flame suitable for welding, forsupplying an oxyhydrogen torch, and the like.

In applications where both the separated hydrogen and separated oxygenmay be required for different purposes, the generation, storage,distribution and/or heating (e.g., cooking) system may directindependently both gases to their appropriate process uses. An examplecould be an electrolyzer on a submarine where the hydrogen may be usedfor a burner, and the oxygen used in the submarines air circulationsystem, and the like. In yet other embodiments the oxygen and hydrogenthat have been separated during the hydrolysis process may need to berecombined under a protocol that produces a desired combination and rateof the combination of oxygen and hydrogen. One such example isOxy-Hydrogen welding.

In embodiments, other examples of time-shifted uses of electrolyzerproducts that may benefit from and/or include hydrogen storage mayinclude storing hydrogen in its non-compressed state, in its gaseousstate, in its compressed liquid state or combinations thereof in a smalltank that is part of a cooking or other industrial system, in a largertank on or near the cooking system, or transported to very large holdingtanks at a facility that is not nearby. Further examples of hydrogenstorage technology may include absorbing the hydrogen by a substrate.The substrate may then be stored in a small tank or other substratestorage facility that may be part of the cooking system, in a largertank on or near the cooking system, transported to very large holdingtanks at a facility that is not nearby, or distributed across aplurality of small, medium, and large storage facilities that mayfacilitate local access to the stored energy. At the appropriate time,the substrate may be heated and the hydrogen may return to its originalgaseous state.

Cooking and other heating systems that may use hydrogen as one of aplurality of sources of fuel may participate in automatically selectingamong the sources of fuel. These systems may include processingcapabilities that are connected to various information sources that mayprovide data regarding factors that may be beneficial to consider whendetermining which energy source to select. Determining which energysource to select may be based, for example on a single factor, such as acurrent price for one or more of the sources of energy. An energy sourcethat provides sufficient energy at a lowest current price may beselected. In embodiments, a cooking or other heating system mayautomatically, under computer control, be configured for the selectedsource of energy. In an example, if hydrogen is selected, connections toa source of hydrogen may be activated, while connections to othersources may be deactivated. Likewise, burners, heater controls, heat andsafety profiles, cooking times, and a range of other factors may beautomatically adjusted based on the selected energy source. If during acooking or heating operation, another source of energy is found to beless costly (such as electricity), systems may automatically bereconfigured for use of the other source of energy. Gas-fired heatersmay be disabled and electric heating elements may be energized tocontinue the cooking and/or heating operation with minimal interruption.Such hybrid energy source cooking and/or heating processes may require adistinct protocol for completing a cooking or heating process based onthe new source of energy.

Alternatively, automatic selection of a fuel source may be based on amultitude of factors. These factors may be applied to a fuel sourceselection algorithm that may process individually, in groups, or incombination a portion of the factors. Example factors may include theprice of other energy sources, including energy sources that areavailable to the cooking and heating system as well as those that arenot directly available. In this way, selecting an energy source may bedriven by other considerations, such as which energy source is betterfor the environment, and the like. In embodiments, an automatic energysource selection may be based, at least in part on the anticipatedavailability of an energy source. In embodiments, predictions of energyoutage, such as brownouts, may be based on a range of factors, includingdirect knowledge of scheduled brownouts and the like. Such predictionsmay also be based on prior experience regarding the availability of thesource(s) of energy, which may be applied to machine learning algorithmsthat may provide predictions of future energy availability. Yet otherfactors that may be applied to an algorithm for automaticallydetermining a source of energy may include availability of a source ofwater for producing hydrogen, availability of renewable energy (e.g.,based on a forecast for sunlight, winds, and the like), level and/orintensity of need of the energy, anticipate level of need over a futureperiod of time, such as the next 24 hours and the like. If an anticipateneed over a future period of time includes large swings in demand overthat timeframe, each peak in demand may be individually analyzed.Alternatively, an average or other derivatives of the demand over timemay be used to determine a weighting for the various sources of energy.

In addition to energy selection for direct application to cooking andheating, energy selection for operating a hydrolyzer to produce hydrogenmay be automated. Energy sources that may be included in such anautomated selection process may include solar energy, wind energy,hydrogen energy, sulfur dioxide, electricity (such as from anelectricity grid), natural gas, and the like. In embodiments, analgorithm that may facilitate automatic energy selection may receiveinformation about each energy source, such as availability, costs,efficiency, and the like that may be processed by, for example comparingthe information to determine which energy source provides the best fitfor operating the hydrolyzer in a given time period. By way of thisexample, the algorithm may favor energy sources that are more reliable,more available, and lower costs than those that are less reliable, lessavailable, and costlier. In embodiments, combinations of these threefactors may result in certain sources being selected. If a demand forreliable energy at a particular time is weighted more highly than price,for example, a costlier energy source may be automatically selected dueto it being more reliably available. An automatic fuel selectionalgorithm may also produce recommendations for fuel selection and ahuman or other automated process may make a selection. In an example, anautomated fuel selection algorithm may recommend a fuel that is lesscostly, but may be somewhat less reliable than another source; howevergiven the weighting or other aspects of the available information aboutthe sources, such a recommendation may meet acceptance criteria of thealgorithm.

Methods and systems described herein may be associated with methods andsystems for automatic selection of an energy source, such as a methodfor determining an optimal use of renewable energy (such as solar, wind,geothermal, hydro and the like) or non-renewable fuel. In embodiments, aselection of energy source to power an onsite, stand alone cooking orheating system may be based on a variety of factors including access anddistance to a source of renewable energy source as a primary source,directly to the cooking system. As an example, while production costdata available regarding hydro-based renewable energy may support itsselection, a delivery network may not be in place or may charge asubstantive premium for access to that particular renewable source;therefore hydro-based renewable energy may not be an optimal use.

In embodiments, other factors include pricing and amount of electricityrequired to use the cooking system and electrolyzer and the; ability ofthe source to match up availability with demand for generated power isrequired for both sustained periods of usage as well as short-termrequirements. In embodiments, other factors that may impact an automatedenergy source selection process may include availability and ability toreuse excess heat from the cooking system and/or other nearby industrialfacilities. In embodiments, excess heat may include exhaust heat, sulfurdioxide byproduct and the like that may be used to generate heat througha heat exchange process. In embodiments, another set of criteria fordetermining which energy source may be optimal for use by a cookingsystem as described herein may include comparing the need for short-termaccessibility to power at arbitrary times throughout the day, comparedto limiting timing of demand to power given timing and availability ofpower sources, such as nearby power sources. Sulfur dioxide as a wasteheat byproduct may be used in a heat transfer process to recapture heatfrom the sulfur dioxide gas; however, it may also be applied directly tothe hydrolyzer system to produce hydrogen. In embodiments, the sulfurdioxide gas may be applied directly to the hydrolyzer system to producehydrogen and reduce the sulfur dioxide gas as a tool for environmentalabatement by reducing the amount of the sulfur dioxide gas and use thegenerated hydrogen to burn trash and other items for its removal, forelectricity generation, and the like.

In embodiments, external systems, such as information systems may beassociated with or connected to hydrogen production, storage,distribution, and use systems as described herein. Information systemsmay receive information from all aspects and system processes including,energy selection (such as automated energy selection) including actualresults as compared to predicted results, energy consumption, hydrogengeneration for each type of energy source (solar, hydro-based, wind,exhaust gas, including sulfur dioxide use, and the like), hydrogenrefinement processes, hydrogen storage (including compressed, naturalstate storage, substrate infusion-based, and the like), hydrogendistribution, uses, combinations with other fuel sources (such ashydrogen with another flammable energy medium) and the like, uses of thehydrogen including timing, costs, application environment, and the like.

In embodiments, communication to and from external systems may bethrough exchange of messages that may facilitate remote monitoring,remote control and the like. By way of this example, messages mayinclude information about a source of the message, a destination, anobjective (e.g., control, monitoring, and the like), recommended actionsto take, alternate actions to take, actions to avoid, and the like.

In embodiments, methods and systems related to hydrogen production,storage, distribution and use may include, be associated with, orintegrate improvement features that may provide ongoing improvements insystem performance, quality and the like. In embodiments, improvementfeatures may include process control and heat recovery, flow control andprecision control, safety, reliability and greater service availability,process and output quality including output consistency. Other featuresthat may be provided and/or be integrated with the hydrogen-basedsystems described herein may include data collection, analysis, andmodeling for improvement, data security, cyber security, networksecurity to avoid external attacks on control systems and the like,monitoring and analysis to facilitate preventive maintenance and repair.

In embodiments, integration and/or access to data processing systemsthat also have access to third-party data may be included in the methodsand systems described herein. By monitoring data collected from sensors,time of day, weather conditions, and other data sources may be used withspecific rule sets to trigger activation and/or stoppage of hydrogen use(e.g., cooking) operations. In embodiments, data may be accumulated in acontinuous feedback loop that may capture data for a range of metricsassociated with operations, such as cooking operations and the like. Inembodiments, analysis and control of activation of such a system mayfactor in the actual requirements and timing when a cooking system needsto be used (such as when a meal is being prepared, such as breakfast, orwhen heating is required for an industrial operation, such as at thestart of a new work shift and the like.

In embodiments, data collection, monitoring, process improvement,quality improvement, and the like may also be performed during operationof such a system. In an example, once a cooking system is activated, thesystem may be able to determine the best way to receive the heatrequired to perform the process at hand at that particular moment intime. Receiving the heat required to perform the process may be selectedfrom a variety of heat sources including in-line hydrogen production,stored hydrogen consumption, combined energy utilization and the like.In embodiments, cooking elements with a mix of hydrogen and non-hydrogenheat burners may be automatically controllable so that the system shouldbe able to automatically, using machine learning for example andcontinuous monitoring, decide to use one or the other source or acombination thereof.

Further in this example, a smart cooktop may include burners forhydrogen and for liquid propane. In embodiments, methods and systems forcooking operation may automatically activate the appropriate burnerbased on fuel selection (e.g., hydrogen burner or the liquid propaneburner.). Operating such a cooking or heating system may be done by acomputer enabled controller that may process factors including time ofday, spot-pricing energy costs for each alternative, length of processinvolved, meeting 100% green requirements, potential hazardous use offlame depending on location of cooking system, other security features,and the like. To faciliate continuous improvement during operationalcontrol, data analysis may be performed on any or all aspects of thesystem. In an example, if the electrolyzer is not activated, sensors maycapture information about the liquid propane burner that is being used.In embodiments, this single data capture example indicates that while itis desirable to collect information about all operational aspects toavoid missing information, practical considerations enable more focuseddata collection and analysis. In embodiments, every activity and actionby the cooking system and heating element may be captured, recorded,measured, and used to inform actions such as quality improvement and thelike.

In embodiments, information may be provided for one or more deploymentsof this cooking system to facilitate self-improvement and real-timedecision making. In embodiments, information captured may also be storedand used in time-series analysis and the like to determine patterns thatmay indicate opportunities for improvement. In embodiments, datacaptured for a plurality of deployments may be used for creating andupdating models that may be used for computer-generated simulations andthe like. These models may be applied to design processes and the like.In embodiments, continuous improvement modifications may be activated bymachine-to-machine learning programs, human improvement efforts,instructional improvement and/or modifications, and the like.

While the foregoing written description enables one skilled in the artto make and use what is considered presently to be the best modethereof, those skilled in the art will understand and appreciate theexistence of variations, combinations, and equivalents of the specificembodiment, method, and examples herein. The disclosure should thereforenot be limited by the above described embodiment, method, and examples,but by all embodiments and methods within the scope and spirit of thedisclosure.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions, and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions, orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationswithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, code,and/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient, and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable transitory and/or non-transitorymedia, storage media, ports (physical and virtual), communicationdevices, and interfaces capable of accessing other clients, servers,machines, and devices through a wired or a wireless medium, and thelike. The methods, programs, or codes as described herein and elsewheremay be executed by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of a program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code, and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

In embodiments, one or more of the controllers, circuits, systems, datacollectors, storage systems, network elements, components, or the likeas described throughout this disclosure may be embodied in or on anintegrated circuit, such as an analog, digital, or mixed signal circuit,such as a microprocessor, a programmable logic controller, anapplication-specific integrated circuit, a field programmable gatearray, or other circuit, such as embodied on one or more chips disposedon one or more circuit boards, such as to provide in hardware (withpotentially accelerated speed, energy performance, input-outputperformance, or the like) one or more of the functions described herein.This may include setting up circuits with up to billions of logic gates,flip-flops, multiplexers, and other circuits in a small space,facilitating high speed processing, low power dissipation, and reducedmanufacturing cost compared with board-level integration. Inembodiments, a digital IC, typically a microprocessor, digital signalprocessor, microcontroller, or the like may use Boolean algebra toprocess digital signals to embody complex logic, such as involved in thecircuits, controllers, and other systems described herein. Inembodiments, a data collector, an expert system, a storage system, orthe like may be embodied as a digital integrated circuit (“IC”), such asa logic IC, memory chip, interface IC (e.g., a level shifter, aserializer, a deserializer, and the like), a power management IC and/ora programmable device; an analog integrated circuit, such as a linearIC, RF IC, or the like, or a mixed signal IC, such as a data acquisitionIC (including A/D converters, D/A converter, digital potentiometers)and/or a clock/timing IC.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be configured for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (“SaaS”), platformas a service (“PaaS”), and/or infrastructure as a service (“IaaS”).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (“FDMA”) network or code division multiple access (“CDMA”)network. The cellular network may include mobile devices, cell sites,base stations, repeaters, antennas, towers, and the like. The cellnetwork may be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable transitory and/or non-transitorymedia that may include: computer components, devices, and recordingmedia that retain digital data used for computing for some interval oftime; semiconductor storage known as random access memory (“RAM”); massstorage typically for more permanent storage, such as optical discs,forms of magnetic storage like hard disks, tapes, drums, cards and othertypes; processor registers, cache memory, volatile memory, non-volatilememory; optical storage such as CD, DVD; removable media such as flashmemory (e.g., USB sticks or keys), floppy disks, magnetic tape, papertape, punch cards, standalone RAM disks, zip drives, removable massstorage, off-line, and the like; other computer memory such as dynamicmemory, static memory, read/write storage, mutable storage, read only,random access, sequential access, location addressable, fileaddressable, content addressable, network attached storage, storage areanetwork, bar codes, magnetic ink, and the like.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the Figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable transitory and/ornon-transitory media having a processor capable of executing programinstructions stored thereon as a monolithic software structure, asstandalone software modules, or as modules that employ externalroutines, code, services, and so forth, or any combination of these, andall such implementations may be within the scope of the presentdisclosure. Examples of such machines may include, but may not belimited to, personal digital assistants, laptops, personal computers,mobile phones, other handheld computing devices, medical equipment,wired or wireless communication devices, transducers, chips,calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers, and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosure,and does not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

Implementations of approaches described above may include softwareimplementations, which use software instructions stored onnon-transitory machine-readable media. The procedures and protocols asdescribed above in the text and figures are sufficient for one skilledin the art to implement them in such software implementations. In someexamples, the software may execute on a client node (e.g., a smartphone)using a general-purpose processor that implements a variety of functionson the client node. Software that executes on end nodes or intermediatenetwork nodes may use processors that are dedicated to processingnetwork traffic, for example, being embedded in network processingdevices. In some implementations, certain functions may be implementedin hardware, for example, using Application-Specific Integrated Circuits(ASICs), and/or Field Programmable Gate Arrays (FPGAs), thereby reducingthe load on a general purpose processor.

Note that in some diagrams and figures in this disclosure, networks suchas the internet, carrier networks, internet service provider networks,local area networks (LANs), metro area networks (MANs), wide areanetworks (WANs), storage area networks (SANs), backhaul networks,cellular networks, satellite networks and the like, may be depicted asclouds. Also note, that certain processes may be referred to as takingplace in the cloud and devices may be described as accessing the cloud.In these types of descriptions, the cloud should be understood to besome type of network comprising networking equipment and wireless and/orwired links.

The description above may refer to a client device communicating with aserver, but it should be understood that the technology and techniquesdescribed herein are not limited to those exemplary devices as theend-points of communication connections or sessions. The end-points mayalso be referred to as, or may be, senders, transmitters, transceivers,receivers, servers, video servers, content servers, proxy servers, cloudstorage units, caches, routers, switches, buffers, mobile devices,tablets, smart phones, handsets, computers, set-top boxes, modems,gaming systems, nodes, satellites, base stations, gateways, satelliteground stations, wireless access points, and the like. The devices atany of the end-points or intermediate nodes of communication connectionsor sessions may be commercial media streaming boxes such as thoseimplementing Apple TV, Roku, Chromecast, Amazon Fire, Slingbox, and thelike, or they may be custom media streaming boxes. The devices at theany of the end-points or intermediate nodes of communication connectionsor sessions may be smart televisions and/or displays, smart appliancessuch as hubs, refrigerators, security systems, power panels and thelike, smart vehicles such as cars, boats, busses, trains, planes, carts,and the like, and may be any device on the Internet of Things (IoT). Thedevices at any of the end-points or intermediate nodes of communicationconnections or sessions may be single-board computers and/or purposebuilt computing engines comprising processors such as ARM processors,video processors, system-on-a-chip (SoC), and/or memory such as randomaccess memory (RAM), read only memory (ROM), or any kind of electronicmemory components.

Communication connections or sessions may exist between two routers, twoclients, two network nodes, two servers, two mobile devices, and thelike, or any combination of potential nodes and/or end-point devices. Inmany cases, communication sessions are bi-directional so that bothend-point devices may have the ability to send and receive data. Whilethese variations may not be stated explicitly in every description andexemplary embodiment in this disclosure, it should be understood thatthe technology and techniques we describe herein are intended to beapplied to all types of known end-devices, network nodes and equipmentand transmission links, as well as to future end-devices, network nodesand equipment and transmission links with similar or improvedperformance.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platforms. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable transitory and/or non-transitorymedia, storage media, ports (physical and virtual), communicationdevices, and interfaces capable of accessing other clients, servers,machines, and devices through a wired or a wireless medium, and thelike. The methods, programs, or codes as described herein and elsewheremay be executed by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (“SaaS”), platformas a service (“PaaS”), and/or infrastructure as a service (“IaaS”).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (“FDMA”) network or code division multiple access (“CDMA”)network. The cellular network may include mobile devices, cell sites,base stations, repeaters, antennas, towers, and the like. The cellnetwork may be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable transitory and/or non-transitorymedia that may include: computer components, devices, and recordingmedia that retain digital data used for computing for some interval oftime; semiconductor storage known as random access memory (“RAM”); massstorage typically for more permanent storage, such as optical discs,forms of magnetic storage like hard disks, tapes, drums, cards and othertypes; processor registers, cache memory, volatile memory, non-volatilememory; optical storage such as CD, DVD; removable media such as flashmemory (e.g. USB sticks or keys), floppy disks, magnetic tape, papertape, punch cards, standalone RAM disks, zip drives, removable massstorage, off-line, and the like; other computer memory such as dynamicmemory, static memory, read/write storage, mutable storage, read only,random access, sequential access, location addressable, fileaddressable, content addressable, network attached storage, storage areanetwork, bar codes, magnetic ink, and the like.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable transitory and/ornon-transitory media having a processor capable of executing programinstructions stored thereon as a monolithic software structure, asstandalone software modules, or as modules that employ externalroutines, code, services, and so forth, or any combination of these, andall such implementations may be within the scope of the presentdisclosure. Examples of such machines may include, but may not belimited to, personal digital assistants, laptops, personal computers,mobile phones, other handheld computing devices, medical equipment,wired or wireless communication devices, transducers, chips,calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers, and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable devices, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples but is to be understood inthe broadest sense allowable by law.

The use of the terms “a,” “an.” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitations of ranges ofvalues herein are merely intended to serve as a shorthand method ofreferring individually to each separate value falling within the range,unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein. All methods described herein may be performed in any suitableorder unless otherwise indicated herein or otherwise clearlycontradicted by context. The use of any and all examples, or exemplarylanguage (e.g., “such as”) provided herein, is intended merely to betterilluminate the disclosure and does not pose a limitation on the scope ofthe disclosure unless otherwise claimed. No language in thespecification should be construed as indicating any non-claimed elementas essential to the practice of the disclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f).

Persons of ordinary skill in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present invention the scope of theinvention is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

It is to be understood that the foregoing description is intended toillustrate and not to limit the scope of the invention, some aspects ofwhich are defined by the scope of the appended claims. Furthermore,other embodiments are within the scope of the following claims.

What is claimed is:
 1. A monitoring system for data collection in anindustrial environment, the system comprising: a data storage structuredto store detection parameters for a plurality of input channels; a datacollector communicatively coupled to the plurality of input channels,wherein the data collector collects data from the plurality of inputchannels based on the detection parameters; a data acquisition circuitstructured to interpret a plurality of detection values from thecollected data, each of the plurality of detection values correspondingto at least one of the plurality of input channels; and a data analysiscircuit structured to analyze the collected data from the plurality ofinput channels, wherein one of the plurality of input channels isconnected to a vibration sensor providing continuous vibration data,wherein when the data analysis circuit detects a measured vibration datavalue outside a predetermined range for the continuous vibration data,the data collector switches from a first detection parameter to a seconddetection parameter for data collection from the one of the plurality ofinput channels connected to the vibration sensor.
 2. The system of claim1, wherein the vibration sensor is a tri-axial sensor connected tomultiple input channels.
 3. The system of claim 1, wherein the vibrationsensor generates a gap-five digital waveform from which the dataanalysis circuit detects an anomalous condition.
 4. The system of claim1, wherein the data analysis circuit analyzes a first and a second ofthe plurality of input channels connected to vibration sensors for arelative phase determination from which the data analysis circuitdetects the measured vibration data value outside the predeterminedrange for the continuous vibration data.
 5. The system of claim 1,wherein the data analysis circuit analyzes frequency components indetecting the measured vibration data value outside a predeterminedrange.
 6. The system of claim 1, wherein the data analysis circuitanalyzes a signal condition for signal-to-noise in detecting themeasured vibration data value outside a predetermined range.
 7. Thesystem of claim 1, wherein the data collector switches from a firstdetection parameter to a second detection parameter by modifying a datacollection trajectory by changing a vibration sensor monitoringlocation.
 8. The system of claim 1, wherein the data collector switchesfrom a first detection parameter to a second detection parameter bymodifying a data collection trajectory by changing a vibration sensorcapability.
 9. A computer-implemented method for data collection in anindustrial environment, the method comprising: accessing storeddetection parameters in a data storage for a plurality of inputchannels; collecting data from a plurality of input channelscommunicatively coupled to a data collector, wherein the collecting datais based on the detection parameters; interpreting a plurality ofdetection values from the collected data by a data acquisition circuit,each of the plurality of detection values corresponding to at least oneof the plurality of input channels; and analyzing the collected datafrom the plurality of input channels, wherein one of the plurality ofinput channels is connected to a vibration sensor providing continuousvibration data, wherein the analyzing comprises detecting a measuredvibration data value outside a predetermined range for the continuousvibration data, and switching from a first detection parameter to asecond detection parameter for data collection from the one of theplurality of input channels connected to the vibration sensor.
 10. Themethod of claim 9, wherein the vibration sensor generates a gap-freedigital waveform from which the analyzing detects an anomalouscondition.
 11. The method of claim 9, wherein the analyzing furthercomprises determining a relative phase difference between a first and asecond of the plurality of input channels, each connected to vibrationsensors, and wherein the detecting the measured vibration data valuesoutside the predetermined range is in response to the relative phasedifference.
 12. The method of claim 9, wherein the analyzing furthercomprises determining frequency components of the continuous vibrationdata, and wherein the detecting the measured vibration data valuesoutside the predetermined range is in response to the frequencycomponents.
 13. The method of claim 9, wherein analyzing furthercomprises performing a signal conditioning to improve signal-to-noiseratio in the continuous vibration data.
 14. The method of claim 13,wherein the performing the signal conditioning further comprises atleast one of filtering the continuous vibration data and anti-aliasingthe continuous vibration data.
 15. The method of claim 14, wherein thedata collector comprises a multiplexer, and wherein the performing thesignal conditioning is performed before switching of the multiplexer.16. An apparatus for monitoring data collection in an industrialenvironment, the apparatus comprising: a data storage structured tostore detection parameters for a plurality of input channels; a datacollector communicatively coupled to the plurality of input channels,wherein the data collector collects data from the plurality of inputchannels based on the detection parameters; a data acquisition circuitstructured to interpret a plurality of detection values from thecollected data, each of the plurality of detection values correspondingto at least one of the plurality of input channels; and a data analysiscircuit structured to analyze the collected data from the plurality ofinput channels, wherein a first one of the plurality of input channelsis connected to a tri-axial vibration sensor, wherein a second one ofthe plurality of input channels is connected to a single axis vibrationsensor, and wherein the tri-axial and single axis vibration sensorsprovide continuous vibration data, wherein when the data analysiscircuit detects a measured vibration data value outside a predeterminedrange for the continuous vibration data, the data collector switchesfrom a first detection parameter to a second detection parameter fordata collection from the one of the plurality of input channels, whereinthe first detection parameter comprises one of a high sampling speed ora low sampling speed, and wherein the second detection parametercomprises the other of the high sampling speed or the low samplingspeed.
 17. The apparatus of claim 16, wherein the data collector isstructured to use interpolation to increase an effective sampling speedof at least one of the input channels.
 18. The apparatus of claim 16,wherein the data collector is further structured to use decimation todecrease an effective sampling speed of at least one of the inputchannels.
 19. The apparatus of claim 16, wherein the data analysiscircuit analyzes the first and the second of the plurality of inputchannels to determine a relative phase difference, and detects themeasured vibration data value outside the predetermined range for thecontinuous vibration data in response to the relative phase difference.20. The apparatus of claim 16, wherein the data analysis circuitperforms a signal conditioning operation to improve a signal-to-noiseratio in the continuous vibration data.